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CongBao

failure-memory

by CongBao
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Server Quality Checklist

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

  • Latest release: v0.7.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one records a failure, the other retrieves lessons. No overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: remember_failure and recall_failure_lessons. The naming is predictable and clear.

    Tool Count4/5

    The server has two tools, which is a bit thin but appropriate for a narrow domain of failure memory. Both tools are essential for the core workflow.

    Completeness4/5

    The server covers the primary actions: remembering a failure and recalling lessons. Missing update/delete operations, but these are not essential for the stated purpose of capturing and retrieving lessons. Minor gap.

  • Average 3.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 20 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 MIT License.

  • This repository includes a README.md file.

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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

  • Behavior2/5

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

    Annotations provide no meaningful behavioral hints (all false), so the description must disclose side effects. It mentions 'record one failure' and 'deduplication review', which hints at a write operation and possible duplicate handling, but it does not explain what happens when a failure is not warranted, whether the operation is reversible, or any other side effects.

    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 with no wasted words. It uses an active verb and clearly outlines the tool's purpose, making it easy to scan.

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

    Completeness1/5

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

    For a tool with 7 parameters, nested schema, no output schema, and unhelpful annotations, this one-sentence description is severely incomplete. It fails to explain parameter usage, return behavior, or conditions for use, leaving the agent to rely solely on the schema structure without semantic guidance.

    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 has 0% description coverage and 7 complex nested parameters, but the description only offers high-level hints like 'root cause', 'repair', and 'proposed lesson'. It does not clarify the required 'summary' and 'classification' parameters, nor the structure of nested objects like 'cause', 'observed', or 'expectation'.

    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 records a single failure along with root cause, repair, deduplication review, and proposed lesson. It uses a specific verb (record) and resource (failure), which distinguishes it from the sibling tool recall_failure_lessons that retrieves lessons.

    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 phrase 'only when warranted' implies a gatekeeping condition, suggesting the tool should not be used for every issue, but it does not explicitly state when to avoid it or mention the sibling tool as the alternative for retrieval. The usage context is implied rather than fully specified.

    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?

    Given the annotations provide no hints (all false), the description carries the burden of disclosing side effects. It explicitly mentions that the tool 'append[s] a privacy-preserving trace', which is a behavioral trait beyond the schema. There is 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 a single, dense sentence that front-loads the core purpose. It avoids unnecessary words, though 'privacy-preserving' could be considered extra detail. Overall, it is effectively concise.

    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?

    This is a complex tool with 10 parameters, no output schema, and no useful annotations. The description covers only a fraction of the input space, failing to explain the structured search criteria and the required combinations (anyOf). It is not complete enough for an agent to invoke it reliably.

    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 description coverage is 0%, so the description must compensate for the 10 parameters. It hints at 'mode' through retrieval types and 'top_k' via 'up to three', but it does not explain the other eight parameters, including the structured fields like 'expected_invariant' or 'cause_layer'. This leaves significant gaps.

    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 identifies the tool's action ('Recall'), the resource ('lessons from global personal memory'), and specifies the scope ('up to three'). It also lists the retrieval modes, which distinguishes it from the sibling tool 'remember_failure'.

    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 this tool is used for recalling previously stored lessons, and the sibling name 'remember_failure' suggests the alternative is for storing. However, no explicit 'when to use' or 'when not to use' guidance is provided, so the context is only implied.

    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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