mcp-claudinho

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.9.1
Disambiguation5/5
Each tool has a clearly distinct purpose: bracket standings, live matches, match details, team lookup, etc. No two tools overlap in functionality.
Naming Consistency5/5
All tools follow the consistent 'get_' prefix and snake_case naming pattern, making them predictable and easy to use.
Tool Count5/5
9 tools cover the essential operations for a football tournament data server without being excessive or insufficient.
Completeness5/5
The tool surface covers team lookup, standings, bracket, fixtures, live matches, match details, market signals, and shareable snippets, addressing key user needs.
Average 4.5/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 2 of 2 community issues answered or closed in the last 6 months
- 129 commits in the last 12 weeks
- Last stable release on August 3, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
View related servers

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds important behavioral details beyond annotations: the tool returns a snippet plus structured data, includes a non-affiliation disclaimer, and market lines are informational. It aligns with readOnlyHint and openWorldHint.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense sentence that efficiently conveys the tool's purpose and key behaviors. It could be broken into shorter sentences, but it is front-loaded and contains no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the 14 parameters and 0 required, the description covers the core functionality and return format. It does not address error cases, but annotations (openWorldHint) and the presence of an output schema reduce the burden.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions already present. The tool description adds minimal additional meaning to parameters beyond what the schema provides, so baseline score is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a polished, copy-pasteable card for various contexts (match, team, group, bracket, date, live), distinguishing it from sibling tools by listing specific use cases and parameters.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies when to use the tool for different snippets and instructs to hand the snippet verbatim. It does not explicitly exclude alternatives but implies usage contexts through parameter examples.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, openWorldHint) already declare safety and open-world nature. Description adds live score/minute overlay when in play, but this is consistent with annotations and does not disclose further behavioral traits like rate limits or data freshness. Bar is lowered by annotations, so 3 is appropriate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences: first states core purpose and live overlay, second gives sourcing guidance, third notes formatting parameters. No wasted words, information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Output schema exists so return values need no explanation. Description covers purpose, id sourcing, and parameter effects. For a read-only tool with good annotations, it is fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are fully documented. Description mentions that tz/lang/flavor affect formatting but does not add meaning beyond what schema provides. Baseline 3 is correct.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves one match by its id, with live score/minute overlay when in play. It explicitly directs users to get the id from get_today or get_live, and differentiates from get_next_fixture for team matches without an id.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit instructions on when to use this tool: get id from get_today or get_live, or use get_next_fixture for team match without id. Also notes that tz/lang/flavor affect formatting, giving context for parameter usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint and openWorldHint. The description adds key behavioral details: fuzzy matching, forgiveness, offline operation, and the return of both a single match and ambiguous candidates. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and efficient. Front-loaded with purpose, followed by usage examples and behavioral notes. Every sentence contributes value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's fuzzy lookup nature and offline design, the description covers purpose, usage, behavioral traits, and return value expectations. Output schema exists, so return fields are partially inferred; overall complete for its complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (single 'query' parameter with description). The description expands with concrete examples (Mexico, MEX, USA, DR Congo) and clarifies fuzzy behavior, adding meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool resolves a team name to FIFA code, flag, and group using fuzzy matching. It highlights its role as the first step for other tools and distinguishes it from siblings by specifying the coordination pattern.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs to use this tool first to convert user input to a code for downstream tools (get_next_fixture, etc.). Mentions offline behavior but does not explicitly state when not to use it; however, the intended workflow is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations by stating the read-only nature, the time window for data availability, and the prohibition on advice. Annotations already mark readOnlyHint and openWorldHint, but the description elaborates on use constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no fluff. It front-loads the core purpose and then adds constraints and advice concisely. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 optional params, output schema exists), the description covers the key behaviors: when to use, what it returns, and informational boundaries. It does not detail every edge case but is sufficient for a read-only tool with good annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. The description adds meaning by explaining the three query modes (matchId, team, date) and their context, which helps the agent understand parameter interplay without repeating schema details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reads prediction-market signals for a match by id, team, or date, and returns market-implied percentages with attribution. It distinguishes itself from sibling tools that focus on raw match data or brackets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies that signals are only shown before and during a match, not after, and warns against adding betting or value advice. It could be improved by explicitly naming sibling tools for alternative queries, but the constraints are clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral details beyond the annotations: it discloses that the tool fetches live data ('live-resolved'), reads from a live overlay, and has a fallback to a bundled schedule. This complements the readOnlyHint and openWorldHint annotations effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: the first concisely states purpose and behavior, the second provides a usage example and fallback note. No wasted words, front-loaded with the most critical information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (not shown but indicated), the description covers all necessary aspects: data source, usage, fallback behavior, and parameter conventions. It is sufficiently complete for an agent to understand and use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers all parameters with descriptions (100% coverage). The description adds minor value by giving examples for the team parameter (e.g., MEX, BRA) and hinting at the flavor parameter's purpose, but it does not substantially enhance understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that it retrieves a team's next match, specifying the distinction between knockout ties (from live overlay) and group fixtures (from bundled schedule). This differentiates it from sibling tools like get_bracket or get_live, providing a specific verb and resource.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit usage guidance: use a 3-letter team code (e.g., MEX, BRA, USA) and mentions fallback behavior if the provider is unreachable. While it does not explicitly list when not to use this tool, the context is clear enough for an agent to choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds context: 'Falls back to structure-only when live data is unavailable' and explains how group slots and winner slots are populated, providing behavioral details beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, no wasted words. Front-loaded with the main purpose, then details on optional filtering and fallback. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists (not shown but indicated), the description covers all necessary aspects: scope, optional filtering, fallback behavior, and parameter hints. No missing context for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for each parameter. The description adds meaning by explaining the stage enum values (R32 through F) and the overall behavior (live scores overlaid, structure-only fallback), complementing the schema well.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Knockout bracket from the Round of 32 through the final, with live scores overlaid', clearly specifying the resource and scope. It distinguishes from siblings like get_standings or get_live by focusing on the bracket view.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explains when to pass an optional stage to filter and notes the fallback behavior when live data is unavailable. Implicitly guides usage for bracket retrieval, but does not explicitly compare to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds transparency by noting results include current score and minute, and that output is 'empty when nothing is live'. This goes beyond annotations in describing the return format and conditional behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no wasted words. Critical information (purpose, when to use, alternative) is front-loaded. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (simple live matches), the presence of an output schema, and annotations covering safety and variability, the description is fully adequate. It covers purpose, usage guidance, parameter semantics, and edge case (empty when live).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by stating 'tz/lang/flavor affect formatting only', clarifying that parameters do not affect data content or filtering. This is useful semantic guidance beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns 'only matches in play right now' with current score and minute, and distinguishes itself from sibling 'get_today' which includes upcoming and finished matches. The verb 'get' plus resource 'live' is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use during matches for in-play state; for a full day's schedule including upcoming and finished, use get_today.' This provides clear when-to-use and when-not-to-use guidance with a named alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true and openWorldHint=true. The description adds that the tool returns live data and falls back to a roster at zero with a degraded flag if unavailable, providing valuable behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no wasted words. Front-loaded with the key purpose and immediately usable by an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of a full output schema and high schema parameter coverage, the description covers all essential aspects: purpose, input options, fallback behavior, and sibling tool differentiation. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds specific guidance for the 'group' parameter ('pass a group letter A–L, or omit for all'), which adds value beyond the schema's description. Other parameters are adequately covered by schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves 'Live cumulative group standings' and specifies the input (group letter A–L or omit for all). It describes output format (ranked rows with team, played, W/D/L, goal difference, points) and distinguishes from siblings like get_today and get_next_fixture.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises when to use alternatives: 'Use get_today for fixtures/scores and get_next_fixture for one team.' Also mentions fallback behavior (roster at zero, flagged degraded) when live standings are unavailable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds valuable behavioral context: live score overlay, timezone rendering, localization, and commentary tone. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: one sentence for core functionality and sibling differentiation, one sentence for parameters. Every sentence earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
All 4 optional parameters are explained, purpose is clear, usage guidelines are explicit, and output schema exists so return values don't need elaboration. The tool definition is fully self-contained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage, baseline is 3. Description adds meaning beyond schema: explains tz for kickoff times, lang with explicit locales and fallback, flavor with default value. This adds moderate value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves all fixtures for a date with live score overlay, and explicitly differentiates from siblings by naming alternatives like get_live, get_next_fixture, and get_match.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance: 'Use this for a whole day's card; for only in-play matches use get_live...' and clearly specifies alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![claudinho MCP server](https://glama.ai/mcp/servers/arturogarrido/claudinho/badges/card.svg)](https://glama.ai/mcp/servers/arturogarrido/claudinho)

Score Badge

Copy to your README.md:

[![claudinho MCP server](https://glama.ai/mcp/servers/arturogarrido/claudinho/badges/score.svg)](https://glama.ai/mcp/servers/arturogarrido/claudinho)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/arturogarrido/claudinho'

If you have feedback or need assistance with the MCP directory API, please join our Discord server