Skip to main content
Glama
matematicsolutions

co-eli-mcp

by matematicsolutions
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.3.3

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: search for finding decisions, get for fetching metadata by citation, and coverage for understanding connector limitations. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Two tools follow a clear co_verb_noun pattern (co_search_sentencias, co_get_sentencia), while co_coverage is a noun-only name. This is a minor deviation but the pattern remains readable and predictable.

    Tool Count5/5

    With only 3 tools, the server is tightly scoped to the task of searching and retrieving Colombian constitutional decisions. Each tool earns its place, and the count feels appropriate for the connector's focused purpose.

    Completeness4/5

    The core workflows of searching, retrieving, and verifying decisions are covered. The coverage tool explicitly documents gaps and fallbacks, which mitigates potential missing functionality. A minor gap is the lack of a listing or browsing tool, but search handles that sufficiently.

  • Average 4.2/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 13 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under Apache 2.0.

  • 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.

  • Add related servers to improve discoverability.

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 annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds the 'verify' behavior, implying existence checking, which is beyond 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.

    Conciseness5/5

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

    The description is a single sentence with no wasted words. It is front-loaded with the action and resource, achieving maximum conciseness.

    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 simple one-parameter tool, full schema coverage, and presence of an output schema, the description is complete. It covers the purpose and the parameter sufficiently for an agent to use it 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?

    Schema coverage is 100% and the parameter 'sentencia' is described with an example. The description adds no additional 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's action ('Verify' and 'fetch') and target resource ('Corte Constitucional citation' and 'its metadata'). It distinguishes well from the sibling tool 'co_search_sentencias' which is for searching, while this tool retrieves a specific citation.

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

    Usage Guidelines2/5

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

    No guidance is provided on when to use this tool versus the sibling tool 'co_search_sentencias'. The description only states what it does, leaving the agent to infer usage context.

    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 already provide strong safety guarantees (readOnly, destructiveFalse, idempotent). The description adds minimal extra behavior (search over metadata), but does not mention pagination, result completeness, or query syntax beyond what's in the schema.

    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?

    Single sentence with no filler, front-loaded with verb and resource, every word 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 rich annotations, output schema existence, and simple parameters, the description covers the essential purpose. Could mention query syntax features, but overall complete for a straightforward search tool.

    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 descriptive parameter descriptions. The description does not add significant meaning beyond the schema, so baseline score 3 applies.

    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 uses specific verb 'search' with clear resource 'Corte Constitucional decision metadata', distinguishing it from sibling tool 'co_get_sentencia' which likely retrieves a single decision.

    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 implies usage for free-text queries against decision metadata. While it doesn't explicitly contrast with the sibling, the sibling's name suggests targeted retrieval, so the intended use is reasonably 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?

    Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds meaningful behavioral context: absence of results may indicate a coverage gap rather than absence in the law, and each gap has a fallback. This goes beyond the annotations without contradiction.

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

    Conciseness5/5

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

    The description is compact, front-loaded with the core purpose, then usage guidance, then return structure. Every sentence adds value, with no redundancy or filler.

    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 zero parameters, rich annotations, an output schema, and a description that covers purpose, usage, and return shape (families, as-of note, gaps list), the definition is complete for an agent to select and invoke the tool correctly.

    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?

    The tool has zero parameters, so schema coverage is 100% trivially and no parameter documentation is required. The description still clarifies what the tool returns, which is more relevant than parameter semantics for this parameterless tool.

    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 a specific verb ('Declare') and resource (what the connector covers), and explicitly distinguishes its scope by noting what it does NOT cover. It is clearly differentiated from sibling search tools like co_search_sentencias and co_get_sentencia.

    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 when-to-use guidance: call before telling a user the law 'does not contain' something, and whenever a search comes back empty. It also explains the reason (absence may be a gap in the connector) and mentions fallbacks, giving clear context without needing exclusions.

    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

co-eli-mcp MCP server — quality and maintenance score on Glama

Copy to your README.md:

Score Badge

co-eli-mcp MCP server — quality and maintenance score on Glama

Copy to your README.md:

Latest Blog Posts

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/matematicsolutions/co-eli-mcp'

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