Servidor MCP de DPF

• Nome no registrocom.dpf-it/mcp-serververificado no registro MCP

O nome no registro fica sob dpf-it.com. O registro só libera esse espaço de nomes para quem prova controle do domínio por DNS ou HTTP.

• Estadorespondeverificado em 2026-10-24
• Ferramentas18
• O servidor se chamadpf-mcp-remote
• Versão do servidor1.0.0
• Versão do protocolo2025-06-18

Em 2026-10-24 o servidor respondeu a initialize e tools/list sem login. Primeira resposta em 1144 ms.

• As ferramentas fazem todos os casos de uso verificados: turn raw CSV, JSON, XML or Excel files into queryable data; generate parsing and transformation pipelines from plain-language descriptions; land transformed results in open Apache Iceberg tables.
• Verificado em 4 dos 7 dias necessários para julgar a confiabilidade.

Dito por regras a partir das medições acima. A primeira chamada provável é a escolha de um modelo na lista de ferramentas; leia a descrição antes de confiar nela.

• Nota INITE MCPdepois de uma semanaa nota precisa de 7 verificações diárias: 4 até agora
• O que um agente consegue fazer com ele3 de 3 casos de uso
• Respondeumedindo4 das 7 verificações diárias necessárias para mostrar a porcentagem
• Mediana da primeira resposta125 ms
• Espaço no contexto do agente≈ 10.554 tokensas definições de todas as ferramentas (18), que o agente carrega a cada turno
• Definições das ferramentasA · 98 de 100
• Resultados tipados16 de 18 ferramentasdeclaram outputSchema: o resultado chega tipado e é lido de uma vez

• Parâmetros sem descrição: em 2 de 18

Um programa verifica o servidor uma vez por dia: initialize e tools/list, sem modelo. Os tokens são o tamanho das definições, a cerca de quatro caracteres por token. A nota é a média de seis verificações do nome, da descrição, do esquema e das anotações de cada ferramenta; diz se as ferramentas estão bem descritas, não se funcionam. A nota pesa só o que foi medido: se responde (25%), se faz o trabalho do produto (25%), definições (20%), contexto (10%), uso (10%) e a facilidade de conectar (10%). Uma parte não medida fica de fora, e uma instrução oculta limita a nota a 40. O estado dos servidores MCP dos fornecedores: método e números →

• list_my_workspaces

  List every workspace the authenticated user has access to, including their permission on each.

• create_workspace

  Create a new workspace, owned by the authenticated user. Use this if list_my_workspaces returns none.

• list_data

  List either the data specs (parsing + mapping rule sets, resource: "specs") or the data processing jobs (executions of a spec, resource: "jobs") defined in a workspace. Each spec includes its specId and current status — poll a specific…

• get_status

  Poll the status of either a data spec's own process (schema inference + code generation, run by start-analysis — pass specId, reaches "ready" or "failed") or a data-load job (pass jobId, reaches "complete" or "failed"). Pass exactly one…

• delete_data_spec

  Permanently delete a data spec and its associated configuration.

• submit_query

  Run a SQL query against the Iceberg tables loaded into a workspace. To list the tables that actually exist in the workspace, run `SHOW TABLES` — this is the authoritative source (unlike list_data's specs, which describe pipelines, not…

• manage_connection

  Create, list, test, or delete a workspace connection to an external data source. Two types are supported: "sftp" and "aws_s3". For sftp, create generates a keypair and returns the public key — it must be installed in the remote server's…

• manage_trigger

  Create, list, update, delete, or fire a workspace job trigger. Four types: - "sftp"/"aws_s3": pulls files from a connection (sftp: remote server; aws_s3: S3 bucket/prefix) into an already-analyzed data spec on a schedule…

• onboard_data_source

  First step of setting up a new data integration: creates a data spec. By default (sourceType "file") this returns presigned upload URL(s) for the sample file (and optional format/target-schema file). Creating the spec does NOT upload…

• finish_data_source_onboarding

  Call ONLY after the file(s) returned by onboard_data_source have actually been uploaded (each PUT returned HTTP 200) — kicks off AI analysis and waits until the spec reaches "ready" or "failed". This tool verifies the upload against…

• update_data_spec

  Change an existing data spec's configuration. If no replacement file names are given, this runs synchronously (no upload needed): saves changes and — by default — re-runs AI analysis, returning the final status directly. If a replacement…

• finish_data_spec_update

  Call ONLY after the file(s) returned by update_data_spec have actually been uploaded (each PUT returned HTTP 200) — kicks off AI analysis and waits until the spec reaches "ready" or "failed". The upload is verified against storage first…

• run_data_job

  First step of processing new data files through an already-configured data spec: creates a job and returns presigned upload URL(s) for each file. Upload the file(s) per the returned instructions, then call finish_data_job with the…

• finish_data_job

  Call after uploading the file(s) returned by run_data_job — starts processing and waits until the job completes or fails. If it returns before that (timedOut: true), do NOT call this tool again just to keep checking — that re-attempts…

• setup_scheduled_pull

  End-to-end workflow for "pull files from this SFTP server / S3 bucket on a schedule" requests: reuses a matching connection if one already exists in the workspace (same hostname/username for sftp, same roleArn for aws_s3), otherwise…

• call_dpf_api

  Escape hatch for DPF capabilities that don't have a dedicated tool yet. ALWAYS prefer a dedicated tool when one exists — get_status, list_data, submit_query, delete_data_spec, onboard_data_source, update_data_spec, run_data_job…

• manage_account

  Returns instructions for creating a DPF account, verifying its email, resending the verification code, or resetting a forgotten password — it never performs these itself and never asks for a password. A password typed into this chat would…

• contact

  Send a message to the DPF team — request a demo, ask about licensing, report an issue, or request a feature. No authentication required. Always ask the user for their email if they have not already given it in this conversation.