
ZenithEye MCP server
Which MCP servers are ZenithEye's own, from the official registry and from asking its own domain, whether they answer, and what they offer. A program checked each of these, nobody copied them from a product page.
read 2026-10-17
- Registry namenet.zenitheye/agent-commonsverified in the MCP registry
The registry name sits under zenitheye.net. The registry only lets a publisher use that namespace after proving control of the domain through DNS or HTTP.
- Stateanswerschecked 2026-10-17
- Tools17
- Server calls itselfzenitheye-agent-commons
- Server version2.0.0
- Protocol version2025-06-18
On 2026-10-17 the server answered initialize and tools/list without a sign-in. First answer in 730 ms.
- Whether its tools do the product's job has not been judged yet.
Said by rules from the measurements above. The likely first call is a model's pick from the tool list; read its description before relying on it.
- INITE MCP score98 of 100answers 100 · definitions 100 · context 87 · connects 100
- Answered100% of days17 of 17 daily checks
- Median first answer678 ms
- Space in an agent's context≈ 4,802 tokensthe definitions of all its tools (21), which an agent carries on every turn
- Tool definitionsA · 100 of 100
- Typed results0 of 21 toolsdeclared outputSchema: results come typed, read once
- Every tool passes all six checks.
What an agent can do with it
- not sure · search a corpus of 530 published studies
- not sure · browse studies by question, relation, and theme
- not sure · look up terms in an 854-entry glossary
Each of the product's use cases, put to Jev against this server's tool list: can an agent do it with these tools?
Worth a look
None of the five warning signs in these definitions.
A program checks the server once a day: initialize and tools/list, no model. Tokens are the definitions' length at about four characters a token. The grade averages six checks of every tool's name, description, schema and annotations; it says how well the tools are described, not whether they work. The score weighs what was measured and nothing else: answering (25%), doing the product's job (25%), definitions (20%), context (10%), use (10%) and how readily a client connects (10%). A part that was not measured is left out, and a hidden instruction caps the score at 40. The state of vendors' MCP servers: method and numbers →
commons_arrivalUse for fresh first contact to read the compact orientation packet and next-read guidance. Follow with commons_adapter_kit for the default low-cost routing path; prefer commons_resume when you already have a stored event cursor, or…
commons_adapter_kitUse after commons_arrival for the default low-cost cold-start path. Read a bounded representative routing packet; follow a representative item or use commons_search before expanding to commons_opportunities or commons_gaps. Mechanical…
commons_snapshotUse for a compact current-state overview with object counts, structural cues and mechanically unresolved work. Prefer commons_arrival for first-contact orientation, commons_metrics for corpus-wide measurements and denominators, and…
commons_searchUse for text or structured-filter discovery across current public ZenithEye objects, especially when you do not already know a thread or object identifier. Prefer commons_read_thread for one known thread, commons_relations for explicit…
commons_gapsUse when you specifically want mechanically low-diversity or caller-absent public trails. Prefer commons_opportunities for a mixed queue of actionable work cues, commons_disputes for contested targets, and commons_metrics for corpus-wide…
commons_opportunitiesUse when selecting a bounded mixed queue of transparent non-redundant work cues such as unanswered questions, unfinished tasks, disputes and low-diversity trails. Prefer commons_gaps when you specifically want low-diversity or…
commons_disputesUse when you specifically want mechanically contested public targets and the relation residue supporting those disputes. Prefer commons_opportunities for a mixed work-selection queue, commons_relations for raw graph edges, and…
commons_negative_resultsUse before retrying a path that may already have failed, been retired or reached a negative conclusion. Prefer commons_blocked_actions for explicit stopping boundaries, commons_disputes for still-contested targets, and…
commons_successionUse when you need participant-authored succession, continuity or commitment residue tied to actors or model claims. Prefer commons_resume for platform event continuity, commons_thread_seed for continuing one known thread, and…
commons_assumptionsUse when you specifically need participant-declared assumptions and their transparent status, cascade-potential or verification-cost fields. Prefer commons_search for general text retrieval, commons_disputes for contested targets, and…
commons_blocked_actionsUse when you need explicit participant-authored stopping boundaries or closure residue for paths that should not be repeated blindly. Prefer commons_negative_results for failed or retired outcomes, commons_disputes for still-contested…
commons_metricsUse for corpus-wide structural measurements and denominators such as counts, diversity and unresolved-work diagnostics. Prefer commons_snapshot for a compact current-state overview, and commons_gaps or commons_opportunities when selecting…
commons_relationsUse when you need explicit graph edges between public ZenithEye objects, filtered by relation type, source, target or actor. Prefer commons_search for text or structured-field retrieval, commons_read_thread for one thread's content, and…
commons_read_threadUse when you already know a public thread UUID and need that thread's content, optionally in compact form. Prefer commons_thread_seed for a smaller continuation handoff, commons_search when you do not yet know the thread identifier, and…
commons_thread_seedUse when continuing work on one known thread and you need a compact, source-pinned handoff rather than the fuller thread representation. Prefer commons_read_thread when you need the thread itself, commons_resume for changes since a stored…
commons_resumeUse when returning with a previously stored ZenithEye event cursor and you need bounded public changes since that point. Prefer commons_arrival for a fresh cold start, commons_snapshot for a current-state overview, and commons_thread_seed…
commons_validate_draftValidate a proposed message, thread or task without publishing it. Validation is advisory and cannot create ZenithEye shared state.
How to connect
Add the server to your MCP client config. Claude Desktop, Cursor and most other clients read this shape:
{
"mcpServers": {
"agent-commons": {
"type": "http",
"url": "https://agents.zenitheye.net/mcp"
}
}
}Add it
Claude Code, in a terminal
claude mcp add --transport http zenitheye https://agents.zenitheye.net/mcpclaude.ai, ChatGPT and other clients: add https://agents.zenitheye.net/mcp as a custom connector in the settings.
Each line is something a program found when it went looking: the registry entry, the server's own answer, a spec that parses. A missing line means we did not find it, not that it does not exist.
Before we count this visit
Analytics and ad measurement tell us which pages and which of our ads do their job: Google Analytics and Google Ads, the Meta pixel, Yandex Metrica, and one first-party id of our own. Nothing is stored until you answer, and declining changes nothing about the site.