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Tribunal TRT2: Certidão Eletrônica de Ações Trabalhistas (CEAT) - Processos Digitais

marketplace

The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them. Covers capability requests like "find an MCP that does X", "consulta um CPF", "is there a tool for Y". Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile (every tool with its id + params, pricing, auth) so you pick the right tool_id → invoke RUNS that tool. KEY: invoke works even when the MCP is NOT installed — it runs the tool pontualmente (one-off), without adding the MCP to the toolkit and without bloating the tool list. If the MCP needs a credential/login, invoke returns a connect link; if it is paid and the wallet is empty, invoke returns a checkout/top-up link (the user opens it, then you retry). Use install only to make an MCP PERMANENT in the active toolkit (its tools then show up natively in future sessions); prefer invoke for a single/occasional use. list_tools lists what is callable right now. subscribe/cancel handle per-MCP billing; report_bug sends feedback; request_mcp asks us to build a NEW MCP when nothing fits. Search/describe flag installed_in_toolkit vs installed_in_workspace. Writes (install/uninstall/subscribe/cancel and the one-off install behind invoke) require workspace owner/admin. It also carries the mcp.ai PROMPT LIBRARY, which is about ready-made prompt TEXT rather than MCPs: search_prompts finds one, get_prompt returns its full text with {{variables}} filled, and publish_prompt saves a prompt and returns a shareable mcp.ai/p/ link that opens without login.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryNo
actionNosearch
mcp_idNo
messageNo
tool_idNo
argumentsNo{}
immediateNo
tier_slugNo
prompt_bodyNo
prompt_slugNo
prompt_toolNo
prompt_varsNo{}
conversationNo[]
prompt_titleNo
request_nameNo
cancel_reasonNo
cancel_commentNo
prompt_targetsNo
report_contextNo
prompt_categoryNo
request_detailsNo
prompt_descriptionNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations give only readOnlyHint=false, openWorldHint=true, no idempotency. The description adds substantial behavioral context beyond these: invoke runs one-off without installing or bloating the tool list, credential/wallet states return connect/checkout links with an explicit retry instruction, writes require owner/admin, and publish_prompt returns a login-free share link. No contradiction with annotations. Minor gap: does not describe the output format of invoke/search results.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Every sentence carries real information and the content is front-loaded with the core purpose. However, it is a single ~300-word unbroken paragraph with no bullets, headers, or action breakdown — the 14 actions and their nuances blur together. Length is justified by complexity, but the lack of structure significantly hurts scannability for an agent parsing this description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 14 actions, 23 params, minimal annotations, and no output schema, the description is remarkably complete: the core flow, invoke edge cases (non-installed execution, connect/checkout retry loop), install-vs-invoke decision, permission requirements, installed_in_toolkit vs installed_in_workspace flags, and the entire prompt library subsystem are all covered. Gaps: exact shape of describe's profile output and mechanics of conversation/vars params. Still, it's about as complete as a hub-tool description gets without becoming bloated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description carries the full burden. It meaningfully explains the core workflow parameters (action enum, mcp_id, tool_id, arguments, query, prompt slugs/vars) by mapping them into the search→describe→invoke flow and the prompt library. However, several params remain unexplained (conversation, tier_slug, immediate, request_name, cancel_reason/cancel_comment, prompt_targets). Partial compensation for the 0% coverage, but a 23-param surface deserves more param-level detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states this is the mcp.ai marketplace — the catalog of every MCP/tool AND the mechanism to run them. It explicitly enumerates each action's purpose (search discovers, describe returns a profile, invoke runs, install makes permanent, etc.) and distinguishes itself from sibling tools like authenticate/connect/toolkit_info. For a composite tool with 14 actions, the purpose is exceptionally well defined.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Extremely explicit usage guidance: 'prefer invoke for a single/occasional use' vs 'use install only to make an MCP PERMANENT', the auto-retry flow after connect/checkout links, and 'request_mcp asks us to build a NEW MCP when nothing fits'. It also documents the write-permission requirement (owner/admin). This is model usage guidance — when, when-not, and named alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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