Tribunal TRT22: Consulta Processual
Server Details
Tribunal TRT22: Lookup Processual, official-source lookup. Platform-hosted, pay per query with prepa
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- mcp-dir/tribunal_trt22_processo-mcp
- GitHub Stars
- 0
- Server Listing
- Tribunal TRT22: Consulta Processual
Available Tools
7 toolsauthenticateAIdempotentInspect
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 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds meaningful behavioral context beyond the annotations: it explains the two authentication modes (permanent vs session-only) and that calling with no args returns a link. It does not describe side effects, but annotations already indicate non-destructive, idempotent behavior, and the description is consistent with that.
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 somewhat rambling and includes extraneous branding ('MCP.AI for IDE agents') and multiple clauses. It conveys necessary information but lacks a clean, front-loaded structure; it could be organized into clearer steps or bullet points.
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 tool is simple with one optional parameter and no output schema. The description covers all relevant aspects: how to authenticate, two token-handling options, and the behavior of no-argument calls. It is complete for the tool's complexity.
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 schema has only a single optional string parameter 'token' with no description, so the description carries the full burden. It explicitly explains that 'token' is a JWT pasted by the user, and that omitting it triggers the login-link retrieval, fully compensating for the 0% schema coverage.
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: authenticating the user by logging in through the browser and obtaining an access token. It provides specific steps and distinguishes the tool's role from siblings by focusing on the authentication flow.
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 gives detailed usage instructions: either configure the token in the server config for permanent access, pass the token as a parameter for session-only login, or call with no arguments to receive the login link. It does not explicitly mention alternatives or when not to use, but the guidance is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
connectARead-onlyIdempotentInspect
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 | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds conditional response details (authenticated:true, empty pending[], connect_url, per-install URLs) that help the agent anticipate outputs without contradicting 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?
Two tight sentences lead with the core purpose and then cover the two meaningful states. Every clause adds information, with no filler or redundant restatement.
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 status tool with no output schema, the description covers the main connection states and expected fields. It could be slightly more explicit about partially connected scenarios, but the current coverage is sufficient for basic invocation decisions.
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 zero parameters, so the baseline is 4. The description appropriately explains behavior without needing to document parameter meanings, and the schema offers nothing to clarify beyond this.
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 'Returns connection status and URLs,' specifying a clear verb and resource. It also distinguishes itself from siblings like authenticate and toolkit_info by focusing on connection state rather than performing authentication or general info.
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 provides clear contextual conditions: when all providers are connected vs. when credentials are missing. It does not explicitly name alternatives or state when not to use this tool, but the conditional scenarios give meaningful guidance for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
marketplaceBInspect
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 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses important behavioral traits: writes require workspace owner/admin, invoke works without installation and returns connect/checkout links if credentials or payment are needed, and install makes the MCP permanent. This goes beyond the annotations (openWorldHint=true, destructiveHint=false) and provides useful side-effect context.
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 extremely verbose and repetitive, with the same sentences repeated with minor variations (e.g., the core flow and the 'KEY' note are restated). It includes non-English words like 'pontualmente' and is presented as a run-on paragraph rather than a structured summary.
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 complexity of 23 parameters and multiple actions, the description provides only a high-level overview and lacks detailed instructions for each action (e.g., which parameters are required for each action, what the output looks like). It does not cover all actions like subscribe, cancel, report_bug, and request_mcp in sufficient depth.
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 schema has 23 parameters with 0% coverage, and the description does not map specific parameters to specific actions. While it mentions action names like search and describe, it does not clarify the purpose of fields like prompt_body, prompt_targets, cancel_reason, or request_details. The description adds minimal parameter-level 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 the tool as a marketplace for discovering, describing, invoking, and installing MCPs and prompts. It distinguishes the core actions (search, describe, invoke, install) and mentions capability requests, though it is verbose and includes some irrelevant repetition.
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 provides usage guidance by explaining when to use invoke (one-off) versus install (permanent), and mentions that it covers capability requests. It also clarifies that prompt-related actions are handled via search_prompts, get_prompt, and publish_prompt, giving some differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
report_bugBIdempotentInspect
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 | [] |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate idempotentHint=true, readOnlyHint=false, destructiveHint=false. The description adds that it sends feedback and includes conversation for reproduction, which is useful context. However, it doesn't disclose what happens after submission (e.g., confirmation, rate limits, or that it's a write operation). The description doesn't contradict annotations, but it doesn't add much beyond what annotations imply. With idempotentHint=true, the description could clarify that repeated submissions are safe, but it doesn't.
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, concise and front-loaded with the purpose. The second sentence provides a specific usage instruction. No fluff, but it could be slightly more structured with explicit parameter guidance. Still, it's efficient.
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 tool has 3 parameters, no output schema, and minimal annotations. The description covers the purpose and hints at the conversation parameter but doesn't explain the 'context' parameter or the expected format of 'conversation' (JSON string). For a feedback tool, it's relatively simple, but the lack of parameter semantics and behavioral details (e.g., what happens after submission) makes it incomplete. It's adequate but not thorough.
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 mentions 'conversation array' but the schema defines 'conversation' as a string with default '[]', which is ambiguous. The description says 'Include the conversation array' but doesn't explain the format or that it's a JSON string. It also doesn't explain 'context' or 'message' beyond what the schema provides. The description adds some meaning for 'conversation' but not for 'context' or 'message'. Given 0% coverage, this is a 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 clearly states the tool's purpose: 'Report a bug, missing feature, or send feedback.' It uses a specific verb ('report') and resource ('bug/feature/feedback'), and distinguishes from siblings by focusing on user feedback rather than authentication, marketplace, or tribunal processes. However, it doesn't explicitly differentiate from potential similar tools, but given the sibling list, it's clear 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?
The description implies when to use it (when reporting bugs, missing features, or feedback) but does not explicitly state when not to use it or mention alternatives. It provides a usage hint: 'Include the conversation array with recent messages for reproduction,' which gives context for effective use. No explicit exclusions or alternative tool references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
show_versionARead-onlyIdempotentInspect
Show the current MCP platform and adapter versions.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds no behavioral details beyond what the annotations already provide (readOnlyHint, idempotentHint, destructiveHint). The word 'Show' implies a read-only operation, but this is already covered by annotations, so no extra transparency is contributed.
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 with no unnecessary words. It directly conveys the purpose without verbosity.
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 has no parameters and no output schema, the description fully covers what the tool does. It is complete for its simple functionality, and no further explanation is necessary.
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, so schema coverage is 100% (vacuously). The description does not need to explain parameters, and the baseline score of 3 applies given high schema coverage.
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: displaying current MCP platform and adapter versions. It is specific and distinguishable from sibling tools such as authenticate, connect, or marketplace, which serve 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?
The usage context is clear (obtaining version information), though it does not explicitly state when not to use it or mention alternatives. Since no alternative tool provides version info, the implication is straightforward, but it lacks explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
toolkit_infoARead-onlyIdempotentInspect
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 | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safe read-only behavior is established. The description adds useful context about what state is returned, but does not disclose additional operational details such as whether any network calls are made or whether the state could be stale.
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 sentence that front-loads the primary action ('Returns the current toolkit state') and then efficiently lists the included details with a colon and comma-separated items. Every word earns its place; there is no redundancy or filler.
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 simple, parameterless, read-only introspection tool with strong annotations and no output schema. The description fully covers the tool's purpose and result contents, and the sibling context does not expose any missing usage requirements. It is complete for an agent to invoke correctly.
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 zero parameters, so there are no parameter semantics to clarify. The description meaningfully explains what the tool returns, which is the only relevant semantic content, earning the baseline score for no-parameter tools.
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 ('Returns') with a clear resource ('current toolkit state') and enumerates exactly what information is provided: installed MCPs, connection status, connected accounts, and catalog tool counts. This clearly distinguishes it from siblings like show_version or authenticate by describing the scope of the introspection.
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 clearly implies the tool is used to inspect the current toolkit state, which provides usable context for when to call it. However, it does not explicitly mention when not to use it or name alternatives such as show_version for version-specific information, leaving usage guidance partially implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
tribunal_trt22_processo_consultarARead-onlyIdempotentInspect
Tribunal TRT22: 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 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond the annotations by disclosing that the service is paid (prepaid credits), hosted by the platform, requires no platform credentials, and clarifies data control and LGPD compliance. These are important behavioral traits not covered by the readOnly/idempotent hints, adding substantial value.
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, coherent paragraph that leads with the primary function and then provides operational and legal context. It is not overly verbose, but the information about payment and LGPD could be separated for better scannability. Overall, it is concise and structured adequately for a read-only tool.
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 lacks information about the expected input format (e.g., what 'grau' means) and the nature of the output (e.g., case status, parties, timeline). Since there is no output schema, the description should at least hint at what the response contains, but it does not. This leaves a significant gap in understanding the tool's full behavior.
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 schema has 0% description coverage, and the tool description does not explain the meaning of the parameters 'grau' or 'numero_processo'. The user must infer that 'numero_processo' is the process number, but 'grau' remains undefined. With no compensating explanation, parameter understanding is left entirely to the schema, which itself lacks descriptions, so this is a severe 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 clearly states the tool's purpose: 'Consulta Processual' (procedural consultation) for TRT22, querying official Brazilian sources. It is distinct from sibling tools like authenticate and connect, which are platform utilities, so there is no ambiguity about its role.
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 provides context on when to use the tool (to consult official court data) and mentions practical aspects like payment and lack of credentials, but it does not explicitly exclude other tools or name alternatives. Since the siblings are unrelated, the context is clear enough without explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
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.
Frequently Asked Questions
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as
io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such asio.github.acme/serveralso needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
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Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
Each tool has a clear, distinct purpose: authentication, connection status, marketplace management, bug reporting, version info, toolkit state, and the actual process query. No two tools overlap in function, so an agent can reliably select the right one.
Naming conventions are mixed: simple verbs (authenticate, connect), nouns (marketplace, toolkit_info), verb_noun pairs (report_bug, show_version), and a long domain-specific name (tribunal_trt22_processo_consultar). There is no consistent pattern across tools, making it harder to predict tool names.
While 7 tools is a reasonable number, the majority serve platform management rather than the server's stated purpose of TRT22 process consultation. Only one tool directly addresses the domain, making the count feel mismatched—an agent expecting a focused query tool would find the set bloated with unrelated platform utilities.
The domain surface is severely underdeveloped: there is only a single query tool (tribunal_trt22_processo_consultar) with no search, batch, or complementary operations. The platform tools are comprehensive but irrelevant to the domain's completeness. Agents have no way to list processes, check validity, or retrieve metadata, leaving obvious gaps.