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gfalbarracinr

MCP Chat

by gfalbarracinr
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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: integer addition, document reading, and document editing. Even the two document tools are unambiguous because one reads content and the other modifies it.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun snake_case pattern: add_ints, read_doc_contents, edit_doc_contents. This creates a predictable and readable naming convention.

    Tool Count2/5

    The server is named 'MCP Chat' but contains only three tools unrelated to chat (math and document editing). The count is small and the set feels arbitrary and under-scoped for the stated purpose.

    Completeness1/5

    The tool set is severely incomplete for a chat server—no messaging or chat-related operations exist. Even within the included domains, there are omissions: document tools lack create/delete, and the math tool only supports addition.

  • Average 3.1/5 across 3 of 3 tools scored. Lowest: 2.2/5.

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

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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

  • Behavior1/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries the full burden of behavioral transparency. It only says 'add' without disclosing return format, error handling, type enforcement, or side effects. Additionally, it describes inputs as 'integers' while the schema types them as strings, creating a confusing behavioral implication.

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

    Conciseness3/5

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

    The description is extremely brief and front-loaded, but it contains a grammatical error ('to integers' instead of 'two integers') and lacks any structural organization. It is not a well-formed sentence, and the brevity works against clarity.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The tool is simple and an output schema exists, but the description fails to explain return values, edge cases, or how this tool fits into a larger workflow. The integer/string contradiction also makes it incomplete and potentially misleading for a seemingly trivial operation.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema already provides 100% parameter description coverage ('First number to add', 'Second number to add'), so the baseline would be 3. However, the description adds conflicting information by calling the inputs 'integers' when the schema declares them as strings, which could mislead the agent into passing the wrong type or expecting integer-specific behavior.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's action: adding integers together. However, the phrasing 'Add to integers together' is grammatically awkward, and it does not explicitly distinguish from sibling tools, though the siblings are clearly unrelated (reading/editing documents). Overall, the purpose is understandable.

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

    Usage Guidelines1/5

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

    The description provides no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. There is no mention of context where this tool is appropriate or inappropriate, leaving the agent without any usage heuristics.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries full burden for behavioral disclosure. It does not mention destructive nature, behavior when old_str is not found, case sensitivity, or side effects. The exact-match requirement appears only in the schema, not the description.

    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 the verb front-loaded and no wasted words. It is concise and easily scanned.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The tool has no output schema and no annotations, so the description should explain return values and edge cases. It omits what happens on success/failure, whether it replaces all occurrences, and any prerequisites, making it incomplete for a mutation 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 description coverage is 100%, so baseline is 3. The description does not add extra meaning beyond what the schema already provides for doc_id, old_str, and new_str.

    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 edits a document by replacing a string, using the specific verb 'Edit' with the resource 'document' and the operation 'replacing a string'. This distinguishes it from sibling tools like read_doc_contents (read) and add_ints (arithmetic).

    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 on when to use this tool vs. alternatives, no prerequisites, and no exclusions. The description merely states the action without context on appropriate usage scenarios.

    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?

    No annotations are present, so the description carries the burden. It communicates the core read-only action and return format, but omits details about error handling, permissions, or potential side effects. The term 'read' implies safety, but this is not explicitly stated.

    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, front-loaded sentence that efficiently states the action and return type. No superfluous words or redundant information, making it highly concise.

    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?

    For a simple tool with one parameter and no output schema, the description covers the essentials: what it acts on and what it returns. It lacks error handling details, but given the low complexity, it is sufficiently complete for basic usage.

    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 provides a complete description of the doc_id parameter (100% coverage). The tool description adds no additional parameter semantics, so the baseline score of 3 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 action ('read'), the resource ('document'), and the return type ('string'). It distinguishes itself from siblings like edit_doc_contents and add_ints by focusing on retrieval, 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.

    Usage Guidelines3/5

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

    The description implies usage for reading documents but does not explicitly compare with edit_doc_contents or specify when to choose this tool over alternatives. The context is clear from the verb, but no direct guidance is given.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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