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Server Quality Checklist

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  • Latest release: v1.4.83

  • Disambiguation3/5

    Memory_search and recall_memory both retrieve memories but with different output formats, causing potential confusion. Memory_get is clearly for full content after search, but the presence of two similar retrieval tools may lead to misselection.

    Naming Consistency2/5

    Tool names mix noun_verb (memory_get, memory_search) and verb_noun (store_memory, update_memory, recall_memory) patterns. Recall_memory lacks the 'memory_' prefix, breaking consistency further.

    Tool Count4/5

    Five tools is a reasonable count for a memory system, covering retrieval and modification. The three retrieval tools are slightly redundant but still within acceptable bounds for a specialized server.

    Completeness3/5

    The set includes store, multiple reads, and update, but lacks a delete tool, which is a notable gap for full lifecycle management. The two-stage retrieval mechanism adds depth but also redundancy.

  • Average 4.1/5 across 5 of 5 tools scored.

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

    • 3 of 4 community issues answered or closed in the last 6 months
    • 146 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
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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

  • 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 discloses key behavioral traits: the return format (list with IDs) and the scoping of memories via the API key parameter (private vs shared). It does not mention read-only nature or side effects, but for a retrieval tool this is adequate.

    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 four sentences, all front-loaded with the main action and essential details. Every sentence contributes unique value: the action, the content type, usage context, and return format. No wasted words.

    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 retrieval tool with 5 parameters and no output schema, the description adequately explains the return format (list of memories with IDs). It could benefit from mentioning what fields each memory contains, but given the lack of nested objects and enums, it is still complete enough for an agent to use.

    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 the baseline is 3. The description does not add additional meaning beyond what is already in the schema's parameter descriptions. The schema itself sufficiently documents each parameter's purpose.

    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 it retrieves memories relevant to a query, with specific mention of user preferences, past instructions, and facts. However, it does not distinguish itself from sibling tools like memory_get or memory_search, which likely have similar functionality.

    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 explicitly says 'Call this when persistent context would help answer the current request,' providing a when-to-use condition. However, it does not specify when not to use it or mention alternative tools among the siblings, so the guidance is implied but not comprehensive.

    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 provided, so description carries full burden. It adds a behavioral assurance (no conflict with recall_memory) but lacks details on error handling, side effects, or persistence guarantees. The schema provides some behavioral info like decay rates and importance ranges, but description itself is minimal.

    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 with purpose, no extraneous information. 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?

    For a simple store operation with 8 well-documented parameters and no output schema, the description is adequate. It covers when to use and a behavioral distinction. Could mention return value or failure modes, but overall complete enough.

    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 each parameter already has a detailed description. The tool description does not add meaning beyond the schema. Baseline 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?

    Description clearly states the tool stores a new memory about the user and specifies concrete use cases (fact, preference, instruction, past failure, strategy). It also distinguishes from sibling recall_memory by noting no conflict.

    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 states when to use ('Use when you learn...'), and clarifies no conflict with recall_memory. However, it does not mention when not to use or provide alternatives among siblings like memory_search or update_memory.

    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?

    With no annotations provided, the description bears full burden. It adds value by explaining merge vs. replace semantics, but lacks details on error handling, idempotency, or side effects. Adequate but not comprehensive.

    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?

    Extremely concise: two sentences that front-load the core purpose and follow with usage guidance. Every sentence is informative and not redundant.

    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?

    Despite low complexity, the description omits important context: it does not mention the 'importance' parameter (which the schema marks as mandatory) nor the return value. For a mutation tool, this leaves gaps in understanding the full behavior.

    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 clear parameter descriptions. The tool description does not add extra parameter semantics beyond the schema, so 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 verb ('merge or replace') and resource ('existing memory by its ID'). It distinguishes from sibling tools (memory_get, memory_search, recall_memory, store_memory) by specifying it is for updating existing memories.

    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 provides two specific use cases: replace when outdated, merge when adding detail. This guides the agent on when to use this tool vs. alternatives, even though it does not explicitly list when not to use.

    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?

    Without annotations, the description clearly states it is a read operation returning full content and connected neighborhood, implying non-destructive behavior. No mention of auth or rate limits, but acceptable for a read 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 sentences, no filler, front-loaded with the key purpose and contextual usage. Each 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 no output schema, the description adequately explains what is returned (full content + neighborhood). Could be slightly richer on the structure of the connected region, but sufficient for an agent.

    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 baseline 3. The description adds context about the 'id' coming from memory_search but does not add new meaning beyond the schema for the other parameters.

    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 specifies a clear verb ('Returns'), resource ('FULL content of one memory PLUS its connected graph neighbourhood'), and distinguishes from siblings like memory_search by calling this 'Stage 2'.

    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 says 'Use after memory_search' and explains it provides 'depth on demand without re-reading files', giving clear context but not listing exclusions or alternative tools.

    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, the description carries full burden. It discloses output structure (id, summary, score) and performance characteristic ('cheap to scan'). However, it does not explicitly state non-destructive behavior, though it is implied by a search operation.

    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. Key information is front-loaded: stage, output nature, and usage recommendation.

    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 no output schema, the description sufficiently describes return format and explains workflow with sibling tool. For this complexity level, it is 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 the baseline is 3. Description does not add additional semantic detail beyond what is in the schema for parameters like query, top_k, and user_id.

    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?

    Clearly states it is 'Stage 1 of two-stage recall' returning a compact index of relevant memories (id + short summary + score). Distinguishes from sibling tools like memory_get by specifying it is a cheap-to-scan first step.

    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 instructs to 'Use this first, then call memory_get on the IDs you want, instead of re-reading source files,' providing clear when-to-use and when-not-to-use guidance.

    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:

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  • Evaluate tool definition quality.

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