Tribunal TRT15: Lookup Processual, official-source lookup. Platform-hosted, pay per query with prepa
MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header Authorization: Bearer <token> for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "" } after the user pastes, or with no args to get the link.
| Name | Required | Description | Default |
|---|---|---|---|
| token | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate non-read-only, idempotent, non-destructive. The description adds behavioral context: it initiates a login, supports a non-expiring config approach vs a session-only approach, and expects a JWT. No contradiction; it reasonably discloses the main state-changing nature of the operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded, delivering the essential workflow in a few sentences. Slight redundancy/preamble ('MCP.AI for IDE agents') and the awkward phrase 'log in in the browser' prevent a perfect score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one optional parameter and no output schema, the description covers the primary use cases, token sourcing, and two authentication paths. It does not mention the response after a successful token submission, but this is a minor gap for such a simple auth flow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema only defines an optional string 'token' with no description. The description compensates well by explaining the token's format, how to pass it, and the no-args behavior for obtaining the link.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly explains what the tool does: authenticates to MCP.AI via a browser-generated token, with two modes (config header or session token). It does not explicitly distinguish itself from sibling tools like 'connect', but the resource and action are specific enough.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear guidance on when to use the permanent config-header approach versus the session-only token approach, but does not contrast with sibling tools or state when not to use this tool. The context is usable but lacks explicit alternatives/exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns connection status and URLs. When all providers are connected, returns authenticated:true and empty pending[]. When credentials are missing, returns connect_url for the toolkit and per-install URLs.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds significant behavioral detail by specifying the exact output conditions (authenticated vs. missing credentials) and the URLs returned. It describes side effects (none) implicitly. This exceeds the annotation coverage but doesn't cover edge cases like partial connectivity. Score 4.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main action, and provides just enough detail. No redundant wording. It's highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple status tool with no parameters, the description covers the two main outcomes and provides the response shape. It doesn't include error cases or details about the URLs structure, but given the simplicitycars and no output schema, this is sufficient for an agent to understand what it returns. It is complete for its purpose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters. The schema is empty (100% coverage trivially). The description doesn't need to explain parameters. Baseline for 0 params is 4. The description adds no parameter info, but that's not necessary. Score 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: it returns connection status and URLs)Skip. It explicitly describes two distinct output states (all providers connected vs. missing credentials), which uniquely identifies it among siblings like authenticate or show_version. The verb is specific and the resource is well-defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for checking connection status, but it doesn't explicitly compare with alternatives like authenticate or provide guidance on when to use this over other tools. It gives clear behavioral context, but no exclusions or alternative recommendations. This is a clear context but not explicit usage guidance, so a 4 is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No | ||
| action | No | search | |
| mcp_id | No | ||
| message | No | ||
| tool_id | No | ||
| arguments | No | {} | |
| immediate | No | ||
| tier_slug | No | ||
| prompt_body | No | ||
| prompt_slug | No | ||
| prompt_tool | No | ||
| prompt_vars | No | {} | |
| conversation | No | [] | |
| prompt_title | No | ||
| request_name | No | ||
| cancel_reason | No | ||
| cancel_comment | No | ||
| prompt_targets | No | ||
| report_context | No | ||
| prompt_category | No | ||
| request_details | No | ||
| prompt_description | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses behaviors beyond annotations: invoke works even when the MCP is not installed, returns connect/checkout links based on state, requires owner/admin for writes, and explains the one-off install behind invoke. This aligns with annotations (openWorldHint=true, readOnlyHint=false) and adds critical context without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is lengthy but densely packed with essential information, structured logically from overall purpose to core flow, key emphasis, permissions, and prompt library. It is efficient given the tool's complexity, though it might be slightly overwhelming and could benefit from clearer section breaks.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (23 params, no output schema, multiple sub-features), the description is remarkably complete. It covers the discovery-to-invocation flow, permission requirements, retry logic, installed vs. workspace flags, and the prompt library API, leaving little undefined for an agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 23 parameters and 0% schema coverage, the description must compensate. It explains the action enum, core parameters like mcp_id, tool_id, arguments, and prompt library params indirectly. However, several params like 'immediate', 'tier_slug', 'conversation', and 'report_context' are not explicitly described, leaving some ambiguity. The description covers the main flow well but not every parameter in detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies this as the official mcp.ai marketplace for discovering, running, and managing MCPs, as well as hosting a prompt library. It distinguishes itself from sibling tools by covering the full lifecycle (search, describe, invoke, install, billing) and explicitly contrasts with the prompt library functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides explicit guidance on when to use each action: 'prefer invoke for single/occasional use', 'use install only to make permanent', and explains the core flow (search → describe → invoke). It also covers conditional behaviors like retry after connect/checkout links and permission requirements for writes, which is exceptionally actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Report a bug, missing feature, or send feedback. Include the conversation array with recent messages for reproduction.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | ||
| message | Yes | ||
| conversation | No | [] |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey that the operation is non-read-only, non-destructive, and idempotent. The description adds the useful behavioral instruction to include recent conversation messages for reproduction, but it does not disclose side effects such as whether a ticket is created or what happens after submission. No annotation contradiction is present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise, front-loaded sentences with no wasted words. It communicates the action and the key usage instruction efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a side-effectful feedback tool with no output schema and zero schema-level parameter documentation. The conversation hint is useful, but the description fails to explain the required 'message' parameter or the optional 'context' parameter, so an agent cannot fully understand the expected request shape.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaning only for the 'conversation' parameter, noting it should contain recent messages for reproduction. The required 'message' parameter and optional 'context' parameter receive no semantic explanation, leaving a significant gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Report') and clearly identifies the resource targets: a bug, missing feature, or feedback. This distinguishes it from sibling tools like authenticate, marketplace, and show_version, which have clearly different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives clear context for when to use the tool (bug reporting, feature requests, feedback) and directs users to include the conversation array for reproduction. It does not explicitly name alternatives or exclusions, but sibling tools are unrelated enough that this is not a major gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Show the current MCP platform and adapter versions.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and non-destructive behavior. The description adds specific context about what versions are shown, going slightly beyond the annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the action ('Show') and is free of unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description adequately conveys the purpose and implied output (version numbers). Since there is no output schema, it does not specify exact return formats, but for a simple version check, this is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the schema fully covers them. The description adds no additional parameter information, which is neutral; a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: showing the current MCP platform and adapter versions. It is specific and distinguishable from sibling tools like toolkit_info, which likely covers broader information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives. Although it is obvious for version checks, there is no explicit guidance on scenarios or exclusions, which is a minor gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns the current toolkit state: installed MCPs, their connection status, the accounts connected to each one, and how many catalog tools each exposes.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds value by specifying exactly what state information is returned, including the 'current' or live nature of the snapshot, without contradicting the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, front-loaded with 'Returns', and every phrase adds specific content: installed MCPs, connection statuses, connected accounts, and catalog tool counts. No redundant wording or padding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, read-only informational tool with good annotations and no output schema, the description fully covers what the agent can expect. It names all meaningful output categories without needing a formal output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero properties, so the description carries no parameter burden. Baseline for a parameterless tool is 4; no further parameter explanation is necessary.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: 'Returns the current toolkit state' and enumerates exactly what aspects are included (installed MCPs, connection status, accounts, catalog tool counts). This clearly distinguishes it from sibling tools like authenticate, connect, marketplace, and show_version.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use case is clear: it is the read-only status/inspection tool for the toolkit, with no parameters. It does not explicitly state when-not-to-use or name alternatives, but for a zero-parameter informational tool the context is strong enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tribunal TRT15: Consulta Processual, consulta em fonte oficial. Hospedado pela plataforma, sem credenciais da plataforma, pague por consulta com crédito pré-pago. Consulta informação de fontes e órgãos oficiais brasileiros (a mesma disponível ao cidadão), não é dado sigiloso. O cliente é o controlador dos dados e responde pela finalidade legítima (LGPD).
| Name | Required | Description | Default |
|---|---|---|---|
| grau | No | ||
| numero_processo | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior; the description adds useful behavior: paid per-query with prepaid credit, no platform credentials needed, official/non-confidential data source, and LGPD responsibilities. This goes beyond the structured annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the tool's core purpose and adds external operational details in a relatively compact way. The legal and credit-related sentences are relevant context, though a short redundancy exists between 'consulta em fonte oficial' and the later reiteration of official sources.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple two-parameter input and available annotations, the description covers purpose, source, payment, and legal context. However, it lacks any description of the input parameters, especially `grau`, and does not describe the expected output, so an agent still has noticeable uncertainty about how to compose a correct request.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description offers no guidance on the `numero_processo` or `grau` parameters. While `consulta processual` implies a process number, the optional `grau` is ambiguous, and nothing explains expected formats, defaults, or how these parameters interact, so the description fails to compensate for the missing schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Consulta Processual' and clearly identifies the resource (TRT15 process) and scope (official source, Brazilian labor court records). It distinguishes itself from generic sibling tools like authenticate, connect, and toolkit_info by naming the specific court and domain.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It indicates this is the way to consult official Brazilian court data via the platform, with prepaid credits and no platform credentials. It does not explicitly state 'use this instead of X', but no sibling tool overlaps with this query capability, and the context strongly implies when it should be selected.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
No tool schema history has been recorded yet.
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Tribunal TRT16: Lookup Processual, official-source lookup. Platform-hosted, pay per query with prepa
Tribunal TRT10: Lookup Processual, official-source lookup. Platform-hosted, pay per query with prepa
Tribunal TRT13: Lookup Processual, official-source lookup. Platform-hosted, pay per query with prepa
Tribunal TRT12: Lookup Processual, official-source lookup. Platform-hosted, pay per query with prepa
Add one secure layer between your agents and this server.
The dedicated query tool and version/status tools are clear, but the set has fuzzy boundaries around connection and catalog inspection: `connect` and `toolkit_info` both expose connection state, and `marketplace` overlaps with the standalone `report_bug` and `toolkit_info` tools through its many sub-actions. The descriptions help somewhat, but an agent could easily pick the wrong administrative tool.
There is no predictable naming pattern: bare verbs like `authenticate` and `connect`, nouns like `marketplace`, compound nouns like `toolkit_info`, and verb_noun names like `report_bug` and `show_version` are mixed together. The single domain tool `tribunal_trt15_processo_consultar` follows a completely different, long Portuguese naming convention.
Seven tools is a reasonable size and fits comfortably within a typical MCP server scope. The split is top-heavy, however, because only one tool actually serves the TRT15 process-consultation purpose while the others are generic platform/infrastructure tools.
The read-only consultation use case is essentially covered by the tribunal-specific query tool, and platform needs such as authentication, connection status, marketplace access, and bug reporting are present. The main gaps are minor: there is no TRT15-specific history, batch lookup, or additional process-related variant, but the core workflow is not blocked.