Fuzzy Memory MCP Server

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose targeting specific operations on the knowledge graph. For example, add_observations vs. delete_observations handle different lifecycle stages, while open_nodes and search_nodes serve distinct access patterns. There is no significant overlap that would cause agent misselection.
Naming Consistency5/5
All tools follow a consistent verb_noun naming pattern using snake_case, such as create_entities, delete_relations, and search_nodes. The naming is predictable and readable throughout the set, with no deviations in style or convention.
Tool Count5/5
With 9 tools, the server is well-scoped for managing a knowledge graph, covering core operations like creation, deletion, reading, and searching. Each tool earns its place without being overly sparse or bloated, fitting typical expectations for this domain.
Completeness4/5
The tool set provides strong coverage for CRUD operations on entities, relations, and observations, along with search and read capabilities. A minor gap exists in update operations (e.g., no update_entities or update_relations), but agents can work around this by deleting and recreating as needed.
Average 2.9/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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
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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?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this is an 'Add' operation (implying mutation) but doesn't describe permissions needed, whether changes are reversible, rate limits, or what happens if entities don't exist. For a mutation tool with zero annotation coverage, this leaves significant behavioral gaps.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core functionality.
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?
For a mutation tool with 1 parameter (but complex nested structure), 0% schema description coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what observations are, how they're structured, what happens on success/failure, or provide any behavioral context needed for safe invocation.
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 schema provides no parameter descriptions. The tool description mentions 'observations' and 'existing entities' but doesn't explain the structure of observations, what 'entityName' refers to, or the format/constraints of observation contents. The description adds minimal value beyond what's implied by the parameter names.
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 action ('Add new observations') and target ('to existing entities in the knowledge graph'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'create_entities' or 'delete_observations', which would require more specific context about when to use each.
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?
The description provides no guidance on when to use this tool versus alternatives like 'create_entities' (for new entities) or 'delete_observations'. It mentions 'existing entities' which implies a prerequisite, but offers no explicit when/when-not instructions or comparison with sibling tools.
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 provided, the description carries the full burden of behavioral disclosure. It states 'Create multiple new entities', implying a write operation, but doesn't cover critical aspects like permissions required, whether creation is idempotent, error handling for duplicates, or rate limits. For a mutation tool with zero annotation coverage, this is a significant gap in transparency, leaving the agent to infer behavior from the name alone.
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, efficient sentence that front-loads the key action and resource without unnecessary words. It avoids redundancy and gets straight to the point, making it easy to parse quickly. Every word earns its place, and there's no wasted verbiage, which is ideal for conciseness in tool descriptions.
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?
Given the complexity of a batch creation tool with no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't address return values, error conditions, or behavioral nuances like whether observations are validated. For a mutation operation in a knowledge graph context, more detail is needed to guide the agent effectively, making this inadequate for the tool's requirements.
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%, meaning the schema provides no descriptions for parameters. The description mentions 'multiple new entities' but doesn't explain what 'entities' entail beyond the schema's structure (name, entityType, observations). It fails to add meaning, such as examples of entity types, format constraints for observations, or how batch processing works. With low coverage, the description doesn't compensate adequately, leaving parameters poorly documented.
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 action ('Create') and the resource ('multiple new entities in the knowledge graph'), making the purpose immediately understandable. It distinguishes from siblings like 'delete_entities' or 'create_relations' by focusing on entity creation rather than deletion or relation management. However, it doesn't specify what constitutes an 'entity' beyond the schema, leaving some ambiguity compared to more detailed descriptions.
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?
The description provides no guidance on when to use this tool versus alternatives like 'add_observations' or 'create_relations'. It doesn't mention prerequisites, such as whether entities must be unique or if there are limits on batch size, nor does it clarify when not to use it (e.g., for single entities or updates). This lack of context makes it harder for an agent to choose appropriately among siblings.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool deletes observations, implying a destructive mutation, but doesn't describe consequences (e.g., whether deletions are permanent, reversible, or affect related data), permissions required, error handling, or rate limits. For a destructive tool with zero annotation coverage, this is a significant gap.
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, efficient sentence with zero waste. It's front-loaded with the core action and target, making it easy to parse quickly. Every word earns its place by conveying essential information without redundancy.
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?
Given the tool's complexity (destructive mutation with 1 parameter but nested structure), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects (e.g., side effects), parameter details, or return values, leaving critical gaps for safe and effective use by an AI agent.
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 schema provides no parameter descriptions. The description mentions 'specific observations from entities' but doesn't explain the input structure (e.g., that 'deletions' is an array of objects with 'entityName' and 'observations' fields). It adds minimal semantic value beyond what's inferred from the tool name, failing to compensate for the coverage gap.
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 action ('Delete') and target ('specific observations from entities in the knowledge graph'), which is a specific verb+resource combination. It distinguishes itself from sibling tools like 'delete_entities' by focusing on observations rather than entire entities. However, it doesn't explicitly contrast with 'add_observations' beyond the verb difference.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing existing observations), exclusions, or comparisons to siblings like 'delete_entities' (for removing entire entities) or 'add_observations' (for adding vs. deleting). Usage is implied by the verb 'delete' but lacks explicit context.
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 provided, the description carries full burden but offers minimal behavioral insight. It indicates a write operation ('Create') but doesn't disclose permissions, side effects, error handling, or response format. The active voice note is trivial and doesn't add meaningful operational context.
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 brief and front-loaded with the core purpose, but the second sentence about active voice adds little value and could be omitted. It's efficient but not perfectly optimized, warranting a 4.
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?
For a write tool with no annotations, 0% schema coverage, and no output schema, the description is inadequate. It misses critical details like mutation effects, error cases, and return values, leaving significant gaps for agent understanding.
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 0%, so the description must compensate but only vaguely mentions 'relations' without explaining the structure or semantics. It implies batch creation but doesn't detail the 'relations' array or its items. Baseline 3 is given as it hints at parameters but lacks substantive clarification.
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 action ('Create multiple new relations') and the resource ('between entities in the knowledge graph'), distinguishing it from siblings like 'create_entities' or 'delete_relations'. However, it doesn't explicitly differentiate from 'add_observations' or other relation-related tools, keeping it at 4 rather than 5.
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?
The description provides no guidance on when to use this tool versus alternatives like 'add_observations' or 'create_entities', nor does it mention prerequisites or exclusions. The only contextual note is about active voice, which is stylistic rather than functional guidance.
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 provided, the description carries full burden for behavioral disclosure. While 'Delete' implies a destructive mutation, it doesn't specify whether deletions are permanent, reversible, require specific permissions, affect downstream data, or have rate limits. The mention of 'associated relations' being deleted is useful but insufficient for a mutation tool.
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, efficient sentence that states the core functionality without unnecessary words. It could be slightly improved by front-loading more critical information (like permanence warnings), but it's appropriately sized for its purpose.
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?
For a destructive mutation tool with no annotations and no output schema, the description is incomplete. It lacks crucial information about behavioral consequences (permanence, side effects), authorization needs, error handling, and what happens to 'associated relations' (e.g., cascade behavior). The context signals indicate moderate complexity that warrants more disclosure.
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 schema fully documents the 'entityNames' parameter. The description adds no additional parameter semantics beyond what's in the schema (e.g., format examples, constraints, or relationship to 'associated relations'). Baseline 3 is appropriate when schema does the heavy lifting.
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 action ('Delete') and target ('multiple entities and their associated relations from the knowledge graph'), which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'delete_observations' or 'delete_relations', which handle different resource types in the same system.
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?
The description provides no guidance on when to use this tool versus alternatives like 'delete_observations' or 'delete_relations', nor does it mention prerequisites, constraints, or typical use cases. It simply states what the tool does without contextual usage information.
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 provided, the description carries the full burden of behavioral disclosure. It states the tool deletes relations but doesn't mention whether this is reversible, what permissions are required, how deletions affect the graph structure, or any rate limits/constraints. For a destructive operation with zero annotation coverage, this leaves significant behavioral gaps.
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, efficient sentence with zero wasted words. It's appropriately sized for a tool with one primary parameter and clear schema documentation, making it easy to parse and understand at a glance.
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?
For a destructive tool with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after deletion, whether there are side effects, what errors might occur, or how to verify success. Given the complexity of graph operations, more context is needed for safe and effective 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 schema fully documents the 'relations' parameter and its nested structure. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter documentation.
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 action ('Delete') and resource ('multiple relations from the knowledge graph'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'delete_entities' or 'delete_observations', which handle different resource types in the same system.
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?
The description provides no guidance on when to use this tool versus alternatives like 'delete_entities' or 'delete_observations', nor does it mention prerequisites, constraints, or appropriate contexts for deletion operations. Usage is implied by the tool name but not explicitly stated.
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 provided, the description carries the full burden of behavioral disclosure. It states the tool 'open[s] specific nodes' but doesn't clarify what 'open' entails operationally—whether it's a read-only retrieval, if it modifies state, requires permissions, or has rate limits. For a tool with zero annotation coverage, this is a significant gap in transparency.
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, efficient sentence that directly states the tool's purpose without redundancy. It is front-loaded and wastes no words, making it 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (involving a knowledge graph) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'open' means in terms of behavior, return values, or potential side effects, leaving the agent with insufficient context for safe and effective 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?
The schema description coverage is 100%, with the parameter 'names' fully documented in the schema as 'An array of entity names to retrieve'. The description adds minimal value beyond this, mentioning 'by their names' but not elaborating on format, constraints, or examples. Baseline 3 is appropriate as the schema does the heavy lifting.
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 action ('open') and target ('specific nodes in the knowledge graph'), specifying they are opened 'by their names'. It distinguishes from siblings like 'read_graph' (general reading) and 'search_nodes' (searching), though not explicitly. However, it lacks full sibling differentiation, as 'open' versus 'read' could be ambiguous without clarification.
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 is provided. The description implies usage for retrieving nodes by name, but it doesn't specify prerequisites, exclusions, or compare to siblings like 'read_graph' or 'search_nodes'. This leaves the agent without clear direction on tool selection in context.
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?
No annotations are provided, so the description carries full burden. It states 'read' (implying safe operation) but doesn't disclose behavioral traits: format of returned data (list, structure, size), whether it's paginated/streamed, performance characteristics, or authentication needs. For a zero-param tool that presumably returns substantial data, this is inadequate.
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 (4 words) and front-loaded. Every word contributes: 'read' (action), 'entire' (scope), 'knowledge graph' (resource). No wasted sentences.
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?
Given zero parameters but no output schema and no annotations, the description is incomplete. It doesn't explain what 'read' returns (e.g., nodes/edges list, serialized format) or behavioral aspects (performance, size limits). For a tool that likely returns complex graph data, more context is needed.
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 tool has zero parameters, and schema description coverage is 100% (empty schema). The description doesn't need to explain parameters, and 'entire knowledge graph' implicitly confirms no filtering parameters exist. Baseline for zero params is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action ('read') and resource ('knowledge graph'), but is vague about scope ('entire' is ambiguous - does this mean all nodes/edges, or a complete dump?). It doesn't distinguish from sibling tools like 'search_nodes' or 'open_nodes' which might also read graph data.
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 versus alternatives like 'search_nodes' or 'open_nodes'. The description implies this reads everything, but doesn't specify use cases (e.g., for analysis vs. specific lookups) or warn about performance with large graphs.
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 carries the full burden of behavioral disclosure. It explains the search is 'fuzzy semantic' and returns confidence scores, which adds useful context. However, it lacks details on permissions, rate limits, pagination, or error handling, which are important for 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?
The description is two concise sentences with zero waste. It front-loads the purpose and efficiently covers key behavioral aspects (fuzzy semantic search, confidence scores) without unnecessary elaboration.
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?
Given the tool's moderate complexity (search operation with one parameter) and no annotations or output schema, the description is adequate but has gaps. It explains the core functionality and return format but lacks details on error cases, performance, or integration with sibling tools, making it minimally viable.
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 schema already documents the single 'query' parameter. The description adds minimal value by implying the query matches against 'entity names, types, and observation content', but this is redundant with the schema's description. Baseline 3 is appropriate as the schema does the heavy lifting.
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 specific action ('performs a fuzzy semantic search'), the target resource ('nodes in the knowledge graph'), and the scope ('based on a query'). It distinguishes itself from siblings like 'open_nodes' (likely for opening specific nodes) and 'read_graph' (likely for reading the entire graph) by focusing on search functionality.
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?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or comparisons to sibling tools like 'open_nodes' or 'read_graph', leaving the agent to infer usage context independently.
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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