Memory MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes targeting specific operations on entities, relations, or observations, with clear boundaries. However, 'open_nodes' and 'search_nodes' could be confused as both involve accessing nodes, though 'open_nodes' appears to retrieve by name while 'search_nodes' uses a query.
Naming Consistency5/5
Tool names follow a consistent verb_noun pattern throughout, such as 'add_observations', 'create_entities', and 'delete_relations'. All tools use snake_case with clear, descriptive verbs, making the set predictable and readable.
Tool Count5/5
With 14 tools, the count is well-scoped for a knowledge graph management server, covering CRUD operations, querying, and maintenance tasks. Each tool earns its place without feeling excessive or insufficient for the domain.
Completeness4/5
The toolset provides comprehensive coverage for knowledge graph operations, including entity and relation management, observation handling, search, and integrity checks. A minor gap is the lack of a tool to update relations, but agents can work around this by deleting and recreating them.
Average 2.9/5 across 14 of 14 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
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.
Add a glama.json file to provide metadata about your server.
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If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'accessed frequently' but doesn't explain what constitutes an 'access', how frequency is measured (e.g., time window), whether this is a read-only operation, what permissions might be required, or what the output format looks like. For a tool with no 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 simple tool and front-loads the core purpose without unnecessary elaboration.
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 has no annotations, no output schema, and a simple but vague purpose, the description is incomplete. It doesn't clarify what 'entities' are in this context, how 'accessed' is defined, or what the output looks like (e.g., list of entities with access counts). For a tool with 2 parameters and siblings that might overlap, more context is needed to guide 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 already documents both parameters ('entityType' as an optional filter and 'minAccessCount' as a required threshold). The description adds no additional parameter semantics beyond what's in the schema, such as examples of entity types or context for what 'minAccessCount' values are typical. Baseline 3 is appropriate when the schema does the heavy lifting.
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 'Find entities that are accessed frequently' states the general purpose (finding frequently accessed entities) but lacks specificity about what 'entities' are, what 'accessed' means in this context, or how 'frequently' is defined. It doesn't distinguish this tool from siblings like 'get_stale_entities' or 'search_nodes' which might also find entities based on different criteria.
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. With siblings like 'get_stale_entities' (likely for infrequently accessed entities) and 'search_nodes' (likely for general entity search), there's no indication of when this specific frequency-based tool is appropriate, nor any mention of prerequisites or exclusions.
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 but offers minimal insight. It indicates a write operation ('create') but doesn't specify permissions required, whether creation is idempotent, potential side effects on the graph, or error handling. This is inadequate for a mutation tool with zero annotation coverage.
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 core action and resource without unnecessary words. It earns its place by conveying the essential purpose, though it could be more informative. There is no wasted verbiage or structural issues.
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 creating multiple entities in a knowledge graph, with no annotations, no output schema, and low parameter coverage, the description is insufficient. It doesn't address return values, error cases, or interaction with sibling tools, leaving significant gaps for the agent to infer behavior in a critical mutation context.
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 description mentions 'multiple new entities' but adds no semantic details beyond what the input schema implies. With 0% schema description coverage, the schema lacks descriptions for the 'entities' parameter itself, and the description fails to compensate by explaining what constitutes valid entities, constraints, or examples. This leaves parameters largely undocumented.
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 resource ('multiple new entities in the knowledge graph'), making the purpose immediately understandable. It distinguishes from siblings like 'update_entity' (modification) and 'delete_entities' (removal), though it doesn't explicitly differentiate from 'add_observations' which might overlap in function.
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, such as whether entities must be unique or if there are constraints on entity types, nor does it compare to siblings like 'add_observations' or 'create_relations' for related operations. This leaves the agent without context for tool selection.
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. It implies a destructive mutation ('Delete') but lacks details on permissions, reversibility, side effects, or error handling. This is inadequate for a deletion tool with zero annotation coverage.
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 no wasted words. It's appropriately sized and front-loaded, directly stating the core action without unnecessary elaboration.
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 destructive nature, lack of annotations, no output schema, and 0% schema coverage, the description is incomplete. It fails to address critical aspects like confirmation prompts, impact on graph integrity, or response format, leaving significant gaps for 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 description must compensate for undocumented parameters. It mentions 'specific observations' and 'entities in the knowledge graph', but doesn't explain the structure of 'deletions' array, what 'entityName' refers to, or how 'observations' are identified. This adds minimal value beyond the schema.
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 verb ('Delete') and resource ('observations from entities in the knowledge graph'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'delete_entities' or 'delete_relations', which would require more specific language about scope.
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_relations', nor does it mention prerequisites or context. It merely states what the tool does without indicating appropriate scenarios or exclusions.
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 ('Add') but doesn't disclose permissions needed, whether it's idempotent, how it handles errors, or what the response looks like. This is inadequate for a mutation tool with zero annotation coverage.
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 core purpose without unnecessary words. Every part earns its place by specifying the action, target, and context concisely.
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 tool with no annotations, no output schema, and 0% schema description coverage, the description is insufficient. It lacks details on behavioral traits, parameter usage, return values, and differentiation from siblings, making it incomplete for effective agent 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 description mentions 'observations' and 'entities', aligning with the single parameter 'observations' in the schema. However, schema description coverage is 0%, so the description doesn't add meaningful details about parameter structure (e.g., that 'observations' is an array of objects with 'entityName' and 'contents'). It partially compensates but leaves key semantics undocumented.
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 'update_entity', which could involve similar operations on entities.
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 'update_entity' (which might also modify entities). It mentions 'existing entities' but doesn't clarify prerequisites or exclusions, leaving usage context implied at best.
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 indicates this is a write operation ('Create'), but doesn't cover critical aspects like permissions needed, whether relations are immutable or editable, error handling, or what happens on success (e.g., returns IDs). The 'active voice' note is trivial and doesn't add meaningful 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.
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' is unnecessary and doesn't earn its place, as it's a stylistic detail irrelevant to tool functionality. Otherwise, it's efficiently structured in two 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 the complexity of creating relations in a knowledge graph, no annotations, no output schema, and low schema coverage, the description is inadequate. It lacks details on behavioral traits (e.g., idempotency, side effects), parameter semantics, and expected outcomes, making it incomplete for effective agent 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 0%, so the description must compensate, but it only vaguely mentions 'relations between entities' without explaining the parameters (relations array with from, to, relationType). It fails to clarify what 'relationType' means or provide examples, leaving the schema to do all the work. This meets the baseline for minimal compensation but doesn't add substantial meaning.
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 resource ('between entities in the knowledge graph'), which distinguishes it from sibling tools like create_entities (creates entities) or delete_relations (deletes relations). However, it doesn't specify what a 'relation' entails beyond being between entities, leaving some ambiguity about the nature of the resource.
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 creating entities instead of relations) or update_entity (for modifying existing entities). The mention of 'active voice' is a stylistic note, not a usage guideline, and there are no exclusions or prerequisites 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 destructive action ('Delete') and cascading effect ('their associated relations'), but lacks details on permissions, irreversibility, error handling, or response format, which are critical 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.
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 scope without unnecessary words, making it easy to parse and understand 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?
For a destructive mutation tool with no annotations and no output schema, the description is insufficient. It misses critical details like permissions, confirmation prompts, error scenarios, and return values, leaving significant gaps 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 'entityNames' fully documented in the schema. The description adds no additional semantic context beyond implying the parameter is used for deletion, meeting the baseline for high schema coverage.
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 verb ('Delete') and resource ('multiple entities and their associated relations'), specifying both the target entities and the cascading effect on relations. However, it doesn't explicitly differentiate from sibling tools like 'delete_observations' or 'delete_relations', which handle different resource types.
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, exclusions, or contextual cues for selection among deletion-related 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?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states the action ('Delete') but doesn't disclose critical traits like whether deletions are permanent, require specific permissions, have side effects on connected entities, or provide confirmation feedback. This is inadequate for a destructive operation with zero annotation coverage.
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 front-loads the core action and resource, making it immediately understandable without unnecessary elaboration.
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 insufficient. It lacks details on behavioral outcomes (e.g., error handling, return values), safety considerations, or integration with sibling tools, leaving significant gaps in contextual understanding for an AI 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 description coverage is 100%, with the single parameter 'relations' well-documented in the schema as an array of objects with 'from', 'to', and 'relationType'. The description adds no additional meaning beyond implying bulk deletion, so it meets the baseline for high schema coverage without compensating value.
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 verb ('Delete') and resource ('multiple relations from the knowledge graph'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'delete_entities' or 'delete_observations', which target different resources in the same knowledge graph 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. It doesn't mention prerequisites (e.g., relations must exist), exclusions, or comparisons to siblings like 'delete_entities' or 'create_relations', leaving the agent to infer usage context from tool names alone.
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 'finds' entities, implying a read-only operation, but doesn't clarify if it's safe, what permissions are needed, how results are returned (e.g., pagination, format), or any rate limits. This leaves significant gaps for a tool that likely queries data.
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 core purpose without unnecessary words. Every part earns its place by specifying the action, resource, and key criterion concisely.
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 (querying entities based on update recency) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'entities' refer to in this context, how results are structured, or any behavioral traits like safety or performance. More context is needed for 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 input schema fully documents both parameters ('days' and 'entityType'). The description adds no additional meaning beyond implying the 'days' parameter defines 'recently', which is already covered in the schema. 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 tool's purpose with a specific verb ('find') and resource ('entities'), and specifies a key criterion ('that haven't been updated recently'). However, it doesn't explicitly differentiate from sibling tools like 'get_frequently_used' or 'search_nodes', which might also retrieve entities based on different criteria.
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 sibling tools like 'get_frequently_used' for active entities or 'search_nodes' for general searches, nor does it specify prerequisites or exclusions. The usage context is implied but not articulated.
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 the tool 'open[s] specific nodes' but doesn't clarify behavioral traits: whether this is a read-only operation (implied by 'open' but not explicit), what 'open' entails (e.g., retrieves details, triggers side effects), error handling for non-existent names, or output format. For a tool with zero annotation coverage, this leaves critical gaps in understanding its 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, efficient sentence that front-loads the core action and resource. Every word earns its place: 'Open' (verb), 'specific nodes' (resource), 'in the knowledge graph' (context), and 'by their names' (parameter hint). There's no redundancy or fluff, 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 moderate complexity (retrieving nodes by name), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'open' means operationally (e.g., returns node details, sets state), potential side effects, error conditions, or return values. For a retrieval tool in a graph context with siblings that handle similar operations, more detail is needed to guide proper 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 description adds minimal meaning beyond the input schema, which has 100% coverage and fully documents the 'names' parameter as 'An array of entity names to retrieve'. The description mentions 'by their names', reinforcing the parameter's role but not providing additional context (e.g., name format, case sensitivity, or limitations). With high schema coverage, the baseline is 3, and the description doesn't significantly enhance understanding.
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 resource ('specific nodes in the knowledge graph'), specifying it operates 'by their names'. It distinguishes from siblings like 'read_graph' (general reading) and 'search_nodes' (search-based retrieval), though not explicitly. However, it doesn't fully differentiate from 'get_frequently_used' or 'get_stale_entities', which might also retrieve nodes, keeping it from a perfect score.
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., nodes must exist), exclusions (e.g., not for creating or updating), or compare to siblings like 'search_nodes' (for fuzzy matching) or 'read_graph' (for bulk access). Without such context, an agent might misuse it in scenarios where other tools are more appropriate.
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 mentions searching based on a query but doesn't explain key traits like whether this is a read-only operation, how results are returned (e.g., pagination, sorting), or any limitations (e.g., rate limits, permissions). This leaves significant gaps for a search 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 a single, efficient sentence that directly states the tool's function without any wasted words. It's appropriately sized and front-loaded, making it easy to 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?
Given the complexity of a search operation with no annotations and no output schema, the description is insufficient. It doesn't cover behavioral aspects like result format, error handling, or usage constraints, making it incomplete for effective tool selection and invocation.
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 input schema has 100% description coverage, with the 'query' parameter well-documented in the schema itself. The description adds minimal value by restating that search is 'based on a query' but doesn't provide additional context like query syntax examples or match criteria beyond what's in the schema, so it meets the baseline.
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 tool's purpose with a specific verb ('search') and resource ('nodes in the knowledge graph'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'open_nodes' or 'read_graph', which might also involve node retrieval, so it doesn't reach the highest score.
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. With siblings like 'open_nodes' and 'read_graph' that might handle node access differently, there's no indication of context, prerequisites, or exclusions, leaving usage ambiguous.
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. It states 'preserving previous observations' which adds useful context about data retention, but doesn't cover other critical aspects: whether this requires specific permissions, if updates are reversible, what happens to metadata not mentioned, error conditions, or response format. For a mutation tool with zero annotation coverage, this is insufficient.
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 - a single sentence that efficiently communicates the core action and a key behavioral constraint. Every word earns its place with no redundancy or unnecessary elaboration. It's front-loaded with the primary 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 mutation tool with 3 parameters, no annotations, and no output schema, the description is incomplete. While concise, it lacks critical information about permissions, error handling, response format, and detailed behavioral expectations. The 'preserving previous observations' hint is helpful but insufficient for a tool that modifies data with multiple optional parameters.
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 all three parameters (metadata, name, observations) with their types and optionality. The description adds minimal value beyond the schema - it implies observations are preserved rather than replaced, but doesn't clarify parameter interactions or provide format examples. 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 verb 'Update' and resource 'entity', specifying it updates an existing entity. It distinguishes from sibling 'create_entities' by focusing on updates rather than creation, but doesn't explicitly differentiate from other update-like tools like 'add_observations' or 'delete_entities'.
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 mentions 'preserving previous observations' which hints at behavior but doesn't specify when to choose this over 'add_observations' for observation management or 'create_entities' for new entities. No explicit when/when-not rules or prerequisites are provided.
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 validates props to prevent hallucination and checks existence in '@props observations,' but doesn't describe what happens during validation (e.g., returns success/failure, error messages), whether it's read-only or has side effects, or any constraints like rate limits. This is a significant gap for a validation tool with zero annotation coverage.
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 and front-loaded, consisting of two sentences that directly state the purpose and method. Every sentence earns its place by clarifying the tool's function without unnecessary details, making it efficient and easy to understand.
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 (validation with 2 parameters), no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., validation results, errors), behavioral traits, or usage context. This leaves gaps for an AI agent to understand how to interpret results or handle failures.
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%, with clear descriptions for both parameters: 'componentName' and 'propsToCheck.' The description adds minimal value beyond the schema by implying the validation context ('@props observations'), but doesn't provide additional syntax, format details, 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 tool's purpose: 'Validate component props to prevent hallucination. Checks if props exist in @props observations.' It specifies the verb 'validate' and the resource 'component props,' with a clear goal of preventing hallucination. However, it doesn't explicitly differentiate from sibling tools like 'verify_graph_integrity' or 'search_nodes,' which might involve validation or checking, so it lacks sibling distinction.
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 mentions checking props in '@props observations,' but doesn't specify context, prerequisites, or exclusions. For example, it doesn't clarify if this should be used before creating entities or as a standalone check, leaving usage ambiguous with siblings like 'verify_graph_integrity.'
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 states 'read' (implying read-only) but doesn't disclose potential impacts like memory usage for large graphs, rate limits, authentication needs, or what 'entire' entails (e.g., all nodes/relations). More context is needed for a mutation-heavy sibling set.
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—'Read the entire knowledge graph' is front-loaded and directly conveys the core action. It earns its place by specifying scope ('entire'), though more detail could improve other dimensions.
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 knowledge graph operations and lack of annotations/output schema, the description is incomplete. It doesn't explain return values, error handling, or how it differs from siblings like 'search_nodes'. For a read tool in a mutation-heavy context, more guidance on usage and behavior is warranted.
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 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description adds no parameter-specific information, which is acceptable here. Baseline is 4 as it doesn't need to compensate for any gaps.
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 'Read the entire knowledge graph' clearly states the action (read) and target resource (knowledge graph). It distinguishes from siblings like 'search_nodes' (filtered reading) and 'get_frequently_used' (partial reading), though not explicitly. The purpose is specific but could better differentiate from similar 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 explicit guidance on when to use this tool versus alternatives like 'search_nodes' (for filtered queries) or 'get_frequently_used' (for partial data). The description implies a comprehensive read, but lacks context on prerequisites, performance implications, or when not to use it (e.g., for 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, the description carries the full burden. It discloses the tool's behavior: it performs verification, identifies orphaned entities, and returns hallucinated names with fuzzy search suggestions. However, it doesn't mention performance characteristics (e.g., time-intensive), side effects, or error handling, leaving gaps for a tool with potential complexity.
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 core purpose and output. Every word contributes value without redundancy, making it easy to parse and understand quickly.
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 no annotations and no output schema, the description provides basic behavioral context but lacks details on return format (e.g., structure of suggestions), error cases, or operational constraints. It's adequate for a simple tool but could be more complete for integrity verification in a knowledge graph 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%, so the schema already documents the single parameter 'maxSuggestions' with its purpose and default. The description doesn't add any parameter-specific information beyond what the schema provides, meeting the baseline for high 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's purpose with specific verbs ('verify integrity', 'check for orphaned entities') and resources ('knowledge graph', 'relationships'). It distinguishes from siblings like 'validate_component_props' or 'get_stale_entities' by focusing on relationship integrity rather than component validation or staleness detection.
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. It doesn't mention prerequisites, timing (e.g., after deletions), or compare to siblings like 'validate_component_props' for other integrity checks. The description implies usage for finding orphaned entities but lacks context on when this is needed.
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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