Verifies a person's identity in Argentina by matching the provided data (DNI, name, birth, address).
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?
The description adds behavioral detail beyond annotations: it explains that calling with no args returns a login link, that the config-header approach yields a non-expiring connection, and that the token arg gives session-only login. This complements the idempotentHint (setting authentication is idempotent) and does not contradict 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?
The description is a single dense sentence followed by a shorter one. It is somewhat verbose but every clause provides useful information. It is front-loaded with the main purpose and includes both usage modes, so it earns a 4 rather than a 5 for brevity.
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 auth tool with one optional parameter and no output schema, the description covers the essential scenarios: permanent setup via config, session-only via token, and obtaining the login link. It does not explain error cases or return values, but that is less critical given the tool's simplicity and the explicit instructions.
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 only defines an optional 'token' string with no description. The description compensates by explaining that token is a JWT for session-only login and that omitting it returns a link. This adds meaningful semantics to the parameter.
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: authentication via access token or browser login. It specifies the verb 'log in' and the resource (authentication), and distinguishes between permanent (config header) and session-only modes. This is a specific and unambiguous purpose.
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 explicit guidance on when to use each mode (permanent config vs session token) and how to invoke the tool (with or without the token argument). It lacks a direct comparison with sibling 'connect', but the usage context 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.
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 declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. The description adds valuable context on response details (authenticated:true, pending[], connect_url) and conditional behavior, which goes beyond the structured fields. No 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 two sentences, front-loads the core function, and provides concise conditional details. Every sentence earns its place with no fluff or redundant information.
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 simplicity (no params, no output schema), the description fully explains return values for both scenarios: all providers connected and credentials missing. It is complete for the agent to understand what will happen.
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 schema is fully covered and the baseline for parameter semantics is 4. The description does not need to explain parameters, and it doesn't introduce any ambiguity.
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 'Returns connection status and URLs' with a specific verb and resource. It also outlines the two possible output states (authenticated with empty pending[] vs. credentials missing with connect_url), making the purpose unmistakable even among sibling tools.
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 and provides conditional behavior based on whether credentials are missing. However, it does not explicitly state when to use this tool instead of alternatives like 'authenticate' or 'toolkit_info', nor does it mention any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verifica a identidade de uma pessoa na Argentina confrontando os dados informados (DNI, nome, nascimento e endereço). Hospedado pela plataforma, sem credenciais, pague por consulta com crédito pré-pago. Consulta informação de ACESSO PÚBLICO em bases e fontes oficiais (a mesma disponível ao cidadão), não é dado privado nem sigiloso. O cliente é o controlador dos dados e responde pela finalidade legítima (LGPD).
| Name | Required | Description | Default |
|---|---|---|---|
| DNI | Yes | ||
| DOB | Yes | ||
| City | Yes | ||
| Name | Yes | ||
| Phone | Yes | ||
| State | Yes | ||
| Address | Yes | ||
| Country | Yes | ||
| Surname | Yes | ||
| PostCode | Yes | ||
| completo | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: no credentials required, pay-per-query with prepaid credit, data source is public/official, and the client is the LGPD data controller. No contradiction with 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?
The description is three sentences with the purpose upfront, followed by payment/hosting and legal context. It is economical and not padded, though the legal sentence adds some length without affecting core usage.
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 complex 11-parameter tool with no output schema. The description explains the high-level purpose, data sensitivity, and legal role, but omits parameter formats, expected return values, and behavior for missing/invalid data. The agent would still have significant uncertainty when invoking the tool.
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 DNI, name, birth, and address, but 11 parameters exist and critical details are omitted: DNI format, date format, the role of Phone, Country, and the optional 'completo' flag. This is insufficient for an agent to correctly fill all required fields.
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 a specific verb ('Verifica') and a clear resource ('a identidade de uma pessoa na Argentina'), and explains the method ('confrontando os dados informados'). This sharply distinguishes it from sibling tools such as authenticate, marketplace, and report_bug.
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 states when to use the tool (Argentine identity verification) and provides relevant context (platform-hosted, no credentials, prepaid credit, public data). It does not explicitly exclude scenarios or name alternatives, but the context is clear enough for an agent to select it appropriately.
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?
Beyond annotations, the description discloses that writes require workspace owner/admin, that invoke works even when the MCP is not installed, and that invoke runs the tool without adding it to the toolkit. It also explains the connect/checkout link behavior and the difference between permanent (install) and one-off (invoke) execution, adding significant 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 long but densely packed, using a logical flow: core marketplace functionality, key behavioral notes, installation vs invocation, permissions, and a separate prompt library section. Every sentence adds value and the structure makes the complexity manageable. It front-loads the main purpose and clearly separates concerns.
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 23 parameters, no output schema, and broad open-world functionality, this description covers the essential context: the core flow, when to install vs invoke, credential/payment handling, permission requirements, and the prompt library with its own sub-actions. It gives the agent enough to operate correctly in most scenarios, including edge cases.
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 0% schema coverage, the description compensates well by explaining the core parameters (action, mcp_id, tool_id, arguments) through the search→describe→invoke flow and prompt-related params via the prompt library section. However, some parameters (e.g., immediate, tier_slug, cancel_reason, conversation) are not explicitly described, though the overall framework helps infer their roles. It adds substantial meaning beyond the bare schema.
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 the official mcp.ai marketplace, acting as both a catalog and execution platform. It lists concrete actions (search, describe, invoke, install, etc.) and explicitly distinguishes itself from siblings by covering capability requests and MCP execution, not just authentication or simple utilities.
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 explicit guidance: use 'invoke' for one-off execution, 'install' for permanent addition, and names alternatives for other tasks ('subscribe/cancel handle billing', 'report_bug sends feedback', 'request_mcp asks us to build a NEW MCP'). It also explains when invoke returns connect or checkout links, teaching the agent how to handle auth and payment flows.
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 disclose readOnlyHint=false, idempotentHint=true, and destructiveHint=false. The description adds the useful context that conversation history is needed for reproduction, but does not disclose any further behavioral details such as external side effects or authentication requirements. It does not contradict 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?
The description is two concise sentences with front-loaded purpose and a direct actionable instruction. Every word earns its place, with 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?
For a simple three-parameter tool with annotations and no output schema, the description covers the core purpose and provides one key usage instruction. However, it leaves the required message parameter and optional context parameter unnamed and unexplained, and does not mention what the result of reporting will be. It is minimally adequate but has clear gaps.
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 only clarifies the 'conversation' parameter ('recent messages for reproduction') but leaves the required 'message' parameter and the optional 'context' parameter semantically vague. The first sentence implies message is the report content, but the description does not explicitly map fields.
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 with specific actions: 'Report a bug, missing feature, or send feedback.' This distinguishes it from sibling tools like authenticate, connect, and marketplace, which serve entirely different functions.
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 guidance on when to use the tool (bug, missing feature, feedback) and instructs the agent to include the conversation array for reproduction. No explicit exclusions or alternatives are named, but none are necessary given the sibling tools are unrelated.
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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the specific scope ('platform and adapter versions') but does not disclose return format or any potential error behavior. With annotations present, this is acceptable but not enhanced.
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 immediately conveys the tool's function. There is zero redundancy, and the structure effectively communicates the essential information.
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 version-information tool with no parameters and no output schema, this description is sufficient. It tells the agent exactly what will be shown (platform and adapter versions) and implies the output format. No additional context is needed.
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, and the schema is complete (100% coverage). The baseline for 0 params is 4; the description does not need to explain parameters. It accurately reflects that no inputs are required.
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 ('Show') and clearly identifies the resource ('current MCP platform and adapter versions'). It is distinct from sibling tools like toolkit_info, which likely provides broader information, making the purpose unambiguous.
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 usage: call this when you need version information. However, it does not explicitly state when to use versus alternatives, nor does it mention any exclusions. There is no competition with siblings, but the guidance remains implicit.
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 and idempotentHint, but the description adds value by specifying exactly what information is returned (MCPs, status, accounts, tool counts), which goes beyond the structured annotations. It does not contradict 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?
The description is a single sentence that front-loads the action ('Returns') and then lists the returned details. Every phrase adds information, with no wasted words.
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 info tool with no output schema, the description fully covers what the tool returns and is sufficient context. The annotations cover safety, and sibling tools provide situational context.
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, and schema coverage is trivially 100%. The description correctly implies no input is needed, so no additional parameter context is required.
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' and clearly defines the resource (toolkit state) with enumerations: installed MCPs, connection status, accounts, and catalog tool counts. This distinguishes it from sibling tools like authenticate or connect, which imply actions.
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 usage for inspecting toolkit state but provides no explicit guidance on when to use this vs alternatives. No exclusions or alternative tool references are present.
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.
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 as io.github.acme/server also 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.json on 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
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
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
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Compares the provided data of a person in Argentina (DNI, name, birth, address) and returns the simi
Registration data for an individual in Argentina from the DNI. Platform-hosted, no credentials, pay
Compares the provided data of a person in Brazil (CPF, name, birth, address) and returns the similar
Check the registration status of a Brazilian CPF against official sources (name, status, birthdate,
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes, but 'connect' and 'toolkit_info' both report connection status, which could cause confusion. The other tools (authenticate, marketplace, identidade_ar_consultar, report_bug, show_version) are well-separated.
Naming is inconsistent: some tools are bare verbs (authenticate, connect), some are nouns (marketplace), some are verb_noun (report_bug, show_version), and one is a Portuguese compound (identidade_ar_consultar). While all use snake_case, the grammatical pattern varies significantly.
7 tools is well within the ideal 3-15 range. Each tool earns its place, covering platform management, identity verification, pricing, and support, without excessive overlap or bloat.
The tool set provides comprehensive coverage of the server's apparent scope: authentication, connection status, identity checking, marketplace search and invocation, bug reporting, version info, and toolkit state. There are no obvious dead ends or missing critical operations.