zme-mcp

Server Quality Checklist

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Latest release: v1.1.0
Disambiguation4/5
Each tool targets a distinct operation: search is for broad knowledge base lookup, recall is specifically for Zeug's own notes, near and why explore the knowledge graph from different angles (neighborhood vs. causal paths), fetch retrieves full content for search hits, and remember writes new facts. Minor overlap exists between search and recall, but the descriptions clearly partition them.
Naming Consistency4/5
All tool names are single lowercase words, giving a clean, consistent visual style. However, the names mix verbs (search, fetch, recall, remember) with non-verbs (near, why), which slightly weakens the semantic consistency of the naming convention.
Tool Count5/5
With 6 tools, the set is well-scoped for a knowledge management server. Each tool addresses a distinct need (lookup, detail retrieval, graph exploration, self-notes, causal analysis, and memory writing), with no apparent redundancy.
Completeness4/5
The main workflows are covered: searching and retrieving content, exploring entity relations, recalling internal notes, tracing causality, and recording durable facts. Missing capabilities like updating or deleting existing facts are minor and don't block primary use cases.
Average 4/5 across 6 of 6 tools scored. Lowest: 3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 commits 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
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.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only and idempotent behavior. The description adds the useful detail that evidence quotes are included, but does not explain how the 'hops' parameter affects traversal or mention any other behavioral caveats. This is adequate but not rich.
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 one-sentence description is extremely concise and front-loaded, containing no fluff. While it sacrifices detail, it is perfectly sized for a simple tool description.
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 the output schema, the description is incomplete: it omits the meaning of 'hops', provides no context for when to use the tool, and does not clarify its relationship to sibling tools. A brief usage example or parameter explanation would make it more complete.
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?
With 0% schema description coverage, the description must compensate. It implies 'entity' is the target entity, but 'hops' is entirely unexplained. This is a significant gap for a knowledge graph traversal tool.
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 indicates the tool returns facts about a single entity, with evidence quotes. It identifies the resource (knowledge graph) and scope (one entity), but uses no explicit verb and does not explicitly differentiate from sibling tools.
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 given on when to use this tool versus search, fetch, recall, why, or remember. The description only says what it does, leaving the agent to infer its place among alternatives.
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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds the requirement for canonical names but does not disclose behaviors like return format (though output schema exists) or handling of missing entities. Adequate disclosure for a safe, read-only 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?
The description is extremely concise, with a useful example and clear instruction. Every word contributes to understanding, and there is no redundancy.
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 tool has an output schema and annotations, but the description omits semantics for the optional max_hops parameter and lacks explicit guidance on when to use 'why' versus siblings. The example is helpful but the overall picture is incomplete for a three-parameter tool.
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?
Schema description coverage is 0%, and the description only clarifies from_entity and to_entity via the example. The max_hops parameter is completely unaddressed, leaving its purpose and effect undocumented. This is a notable gap given the low schema coverage.
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 returns a causal chain between two entities in the knowledge graph, with a concrete example. This distinguishes it from sibling tools like search or fetch by focusing on causal relationships.
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 instruction to use canonical names is explicit and aids correct invocation. The example demonstrates the intended argument order. However, it does not explicitly state when to use 'why' over siblings or mention any exclusions, but the context is clear enough.
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 declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds the useful context that the id is a chunk UUID from search and that full content is returned, but it does not disclose other traits like error behavior or pagination. This is adequate given 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, front-loaded sentence with no filler. It efficiently conveys the action, target, and key parameter context.
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?
With only one parameter, an output schema, and strong annotations, the description covers the essential workflow (post-search retrieval by id). It could mention edge cases or prerequisites, but the simplicity and structured data make it reasonably complete.
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 0%, so the description must compensate. It does by explaining that the 'id' parameter is a chunk UUID returned by search, giving semantic meaning beyond the schema's bare 'string' type. This is sufficient for a single parameter.
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 a specific verb ('Fetch') and identifies the exact resource ('full content of one search result') and the id's origin ('chunk UUID returned by search'). This clearly differentiates it from sibling tools like search and recall.
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 phrase 'returned by search' implies the intended workflow: perform a search first, then use fetch with a result id. This provides clear context, though it does not explicitly state when *not* to use the tool or mention alternatives.
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 read-only, non-destructive, and idempotent behavior, so the description need not repeat that. It adds valuable context about content scope (notes, analysis, compiled research, and wider articles/social posts with wide=true). It doesn't detail return format, but an output schema exists, so that is covered elsewhere.
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?
Three concise sentences deliver purpose, usage context, and parameter guidance without any fluff. The description is front-loaded and each sentence earns its place.
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?
For a simple retrieval tool with annotations and an output schema, the description covers the primary use case and content scope. However, it omits the 'k' parameter and does not mention alternative siblings other than 'search', leaving some ambiguity for distinguishing from tools like 'remember' or 'why'. Overall adequate but with clear gaps.
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?
Schema description coverage is 0%, so the description must carry the burden of explaining parameters. It only explains 'wide' (broaden to articles/social posts), but does not describe 'query' or 'k'. The meaning of 'query' is implicit, but 'k' (result count) is left unexplained, which is a significant gap.
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 recalls Zeug's internal writings (notes, analysis, compiled research) and explicitly contrasts this with search for 'why did we decide X' questions, effectively distinguishing it from a key sibling. The verb 'Recall' and resource are specific.
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: 'Use this (not search) for "why did we decide X" questions.' Also includes a parameter-based guidance ('Set wide=true to also include articles and social posts'). This is clear and actionable for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (all false, providing no safety profile), the description discloses meaningful behavior: the fact survives the session, becomes searchable only after the next ingest run (eventual consistency), and requires OAuth zme:write scope. It also adds a security rule (never store secrets). This is rich, non-obvious behavioral context.
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?
Three concise sentences, each adding distinct value: what it records, when it becomes searchable, and security/auth constraints. No redundant wording, and the core purpose is front-loaded.
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 tool's simplicity (3 scalar params) and the presence of an output schema, the description covers all essential aspects: purpose, durability, indexing behavior, security caveat, and auth requirement. Nothing critical is missing.
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?
Schema description coverage is 0%, and the description does not explain any of the three parameters. The text parameter is somewhat inferable from 'Record a durable fact,' but agent and title have no semantic guidance, so the description fails to compensate for the schema's lack of detail.
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 'Record a durable fact' — a specific verb and resource — and clearly distinguishes this write tool from the retrieval siblings (search, fetch, near, recall, why). The types of facts (pitfall, decision, incident) further refine the purpose.
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?
It provides clear context for when to use the tool (recording durable facts rather than ephemeral ones) and includes important constraints (never store secrets, requires zme:write scope). It does not explicitly name an alternative for retrieval, but the mention of searchability after ingest implies that search is for later use, which is sufficient.
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 readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds value by disclosing the return format ({results: [{id, title, url}]}) and that only summaries are returned, implying the need for a separate fetch call. This is useful behavioral context 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?
Two concise sentences: the first states purpose and return format, the second gives a usage tip and directs to fetch. Every word earns its place; no filler or repetition of schema/annotations.
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?
For a simple search tool with one parameter and an output schema, the description is complete. It covers what the tool does, what it returns, how to proceed for more detail, and how to formulate queries. No essential guidance is missing.
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 schema only defines 'query' as a string with no description (0% coverage). The description compensates by stating 'Natural-language questions work best', implying the query is a natural-language search phrase rather than a structured keyword. This gives meaningful semantic guidance absent from 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 action: 'Search Zeug's knowledge base' with a specific resource. It also distinguishes from siblings by noting that results are summaries and that 'call fetch(id) for full content' — a direct pointer to the appropriate sibling.
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 gives clear context: use this to search, then use fetch for full content. It also advises that natural-language questions work best, which guides how to phrase the query. However, it does not explicitly mention when not to use it or compare with other siblings like near/recall/why.
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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