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MukundaKatta

agentcast-mcp

by MukundaKatta
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clear, distinct purpose: build_retry_prompt handles feedback generation, extract_json extracts JSON from text, and validate_response checks JSON against a spec. There is no functional overlap.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (build_retry_prompt, extract_json, validate_response), making them predictable and easy to understand.

    Tool Count4/5

    With only 3 tools, the server is slightly underpopulated but still appropriately scoped for the narrow domain of agent response validation and retry feedback. It covers the core workflow without unnecessary bloat.

    Completeness4/5

    The tools cover the essential pipeline of extracting JSON, validating it against a spec, and generating retry feedback. A minor gap is the lack of explicit handling of attempt history beyond what build_retry_prompt expects, but that is assumed provided by the client.

  • Average 4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • 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.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It discloses that the tool generates a retry feedback message, but does not detail the output structure, side effects (assumed none), or error behavior. Lacks specifics beyond the basic action.

    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 concise sentences. The first front-loads the main action, the second adds context. No redundant or unnecessary words.

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

    Completeness3/5

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

    The description explains the type of input (attempt history) and the pattern it codifies, but fails to mention the output format or any return value. Given the nested parameters and no output schema, the description is incomplete for full context.

    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%, providing detailed explanations for both parameters (attempts, expected_shape). The description adds contextual value by framing the tool's purpose as retry feedback, but does not introduce new parameter semantics beyond what the schema offers. Baseline 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 tool's purpose: 'produce the retry feedback message' for when the model returned the wrong shape. It specifies the context (non-Node MCP clients) and distinguishes it from siblings (extract_json and validate_response) which focus on extraction or validation, not feedback generation.

    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 explicit guidance on when to use this tool versus alternatives. The description does not mention when-not to use it or provide comparisons to sibling tools. It only implies the scenario (validation error pattern) but lacks direct usage directives.

    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?

    With no annotations provided, the description carries full burden. It transparently explains the fallback strategy: tries whole text, then fenced block, then largest balanced substring, and returns the parsed value plus which strategy succeeded. This is sufficiently detailed for a read-only cleanup tool.

    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 concise sentences, front-loaded with purpose and followed by strategy. Every sentence adds value with no wasted words. Easy for an agent to parse quickly.

    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 simplicity (1 param, no nested objects, no output schema), the description is fairly complete. It explains the return value (parsed value plus strategy). The absence of an output schema is mitigated by the description's mention of the return format.

    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 a clear parameter description. The description adds value by explaining how the text parameter is processed (the extraction strategy), which goes beyond the schema's basic description.

    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 purpose: 'Pull a JSON value out of messy LLM output.' It specifies the verb and resource, and the context of use (LLM output). It distinguishes itself from siblings by focusing on JSON extraction, while siblings deal with retry and validation.

    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 when extracting JSON from messy LLM output, but does not explicitly state when not to use it or mention alternative tools. No exclusion criteria or when-to-use guidance beyond the implied context.

    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?

    Discloses return format, spec details, optional fields, and error string suitability for retry. No annotations provided, so description carries full burden.

    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, front-loaded purpose, no wasted words. Every sentence adds value.

    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?

    Covers validation logic, shape format, return values, and ties to retry feedback. Could mention what happens if input is not parsed JSON, but overall sufficient.

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

    Parameters5/5

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

    Adds meaning beyond schema: explains shape spec format, optional field syntax, and return behavior. Schema coverage is 100% but description enriches understanding.

    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 explicitly states it validates a parsed JSON value against a shape spec, with clear return behavior. It distinguishes from sibling tools (extract_json, build_retry_prompt) by focusing on validation.

    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?

    Implicitly indicates use for validation after parsing, but no explicit when-to-use, when-not, or comparison to siblings.

    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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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.

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