Knowledge Graph Memory Server

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, but 'open_nodes' and 'search_nodes' could cause some confusion as both involve accessing nodes, though 'open_nodes' seems to retrieve specific nodes by name while 'search_nodes' queries based on criteria. The CRUD operations for entities, relations, and observations are clearly separated, reducing major overlap.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case, such as 'add_observations', 'create_entities', and 'delete_relations'. This predictability makes it easy for agents to understand and use the tool set without confusion from mixed conventions.
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. This count is appropriate as it provides comprehensive functionality without being overwhelming or too sparse for the domain.
Completeness4/5
The tool set offers strong coverage with CRUD operations for entities, relations, and observations, plus reading and searching capabilities. A minor gap is the lack of update tools for entities or relations, which might require workarounds like delete-and-recreate, but core workflows are well-supported.
Average 3.1/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
This repository is archived. Archived repositories automatically receive an F maintenance tier.
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.
If you are the author, simply .
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.
Add related servers to improve discoverability.

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To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 the full burden of behavioral disclosure. It states this is a creation operation, implying mutation, but doesn't cover critical aspects like permissions needed, whether it's idempotent, error handling, or rate limits. The active voice note is trivial and doesn't add meaningful behavioral context for tool invocation.
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 to the point with two sentences. The first sentence states the core purpose efficiently, and the second adds a stylistic note. There's no unnecessary fluff, though the active voice guidance could be considered slightly extraneous. It's appropriately sized for a simple tool.
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 has an output schema (which handles return values), no annotations, and low schema coverage, the description is minimally adequate. It covers the basic purpose but lacks usage guidelines, behavioral details, and parameter explanations. For a mutation tool in a knowledge graph context, more completeness would be helpful, but the output schema mitigates some gaps.
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 schema provides no parameter descriptions. The tool description doesn't explain what 'relations', 'from', 'to', or 'relationType' mean beyond what's inferable from the names. It mentions 'entities in the knowledge graph' and 'active voice', but these don't clarify parameter usage or semantics. Baseline is 3 due to 0% coverage, but the description adds minimal 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 action ('Create multiple new relations') and the resource ('between entities in the knowledge graph'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'delete_relations' or 'create_entities', though the verb 'create' implies a distinction. The active voice guidance is stylistic rather than functional.
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' or 'delete_relations'. It mentions active voice as a stylistic preference, but this doesn't help the agent decide between tools based on functional needs or context. There are no explicit when/when-not statements or prerequisites.
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 this is a deletion operation (implying mutation/destructive action) but doesn't describe permissions needed, whether deletions are permanent/reversible, rate limits, or what happens to related data. For a destructive 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 with zero wasted words. It's front-loaded with the core action and target, making it immediately understandable despite its brevity.
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, 0% schema description coverage, but with an output schema, the description is inadequate. It doesn't explain what 'observations' are in this context, how deletions affect the knowledge graph, error conditions, or return values. The existence of an output schema helps but doesn't compensate for the lack of behavioral and semantic 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?
The schema has 0% description coverage, so parameters are undocumented in structured fields. The description mentions 'specific observations from entities' which hints at the 'entityName' and 'observations' parameters, but doesn't explain what constitutes valid entity names or observation values, nor the structure of the 'deletions' array. It adds minimal semantic context beyond what's inferable from 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 ('Delete') and target ('specific observations from entities in the knowledge graph'), which is a specific verb+resource combination. It distinguishes from siblings like 'delete_entities' by focusing on observations rather than entire entities, but 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 like 'delete_entities' or 'add_observations'. It doesn't mention prerequisites, constraints, or typical scenarios for deleting observations rather than other operations.
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. It only states the action without disclosing behavioral traits such as performance implications (e.g., large data returns, rate limits), authentication needs, or what 'entire' means operationally (e.g., pagination, format). This leaves critical gaps for a read 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 a single, efficient sentence with zero waste. It's front-loaded and appropriately sized for the tool's simplicity, making it easy to parse 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 low complexity (0 params, output schema exists), the description is minimally adequate but incomplete. It lacks context on usage versus siblings and behavioral details, which are needed despite the output schema covering return values. This results in a baseline viable but with clear gaps.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, but it implies no inputs are required, aligning with the schema. Baseline 4 is given as it meets expectations for a zero-param tool.
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 ('the entire knowledge graph'), which provides basic purpose. However, it lacks specificity about what 'read' entails (e.g., retrieving all nodes/relations vs. metadata) and doesn't distinguish from siblings like 'search_nodes' or 'open_nodes', making it vague rather than clear.
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 provided on when to use this tool versus alternatives. It doesn't mention scenarios like retrieving full graph data versus filtered searches with 'search_nodes' or accessing specific nodes with 'open_nodes', leaving 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. While 'Add' implies a write/mutation operation, it doesn't specify permissions required, whether the operation is idempotent, what happens on duplicate observations, or any rate limits. The description lacks crucial behavioral context 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 directly states the tool's purpose with zero wasted words. It's appropriately sized for a straightforward tool and front-loads the essential information.
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 this is a mutation tool with no annotations but with an output schema (which handles return values), the description provides basic purpose but lacks important behavioral context. For a tool that modifies a knowledge graph, more information about side effects, constraints, or error conditions would be valuable despite the output schema existence.
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 schema provides no parameter descriptions. The tool description mentions 'observations' and 'existing entities' which aligns with the 'observations' array parameter containing 'entityName' and 'contents' fields. However, it doesn't explain the structure, format, or constraints of these parameters beyond what's evident from the schema itself.
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 scope definition.
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 minimal guidance - it implies this tool is for adding observations to existing entities, but doesn't specify when to use it versus alternatives like 'create_entities' (for new entities) or 'delete_observations'. No explicit when/when-not guidance or prerequisites are mentioned.
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 this is a creation operation but doesn't address permissions needed, whether entities are immutable after creation, potential side effects, rate limits, or error handling. For a write 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 that gets straight to the point with no wasted words. It's appropriately sized for the tool's apparent complexity and front-loads the essential information.
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 this is a write operation with no annotations, 0% schema description coverage, but with an output schema present, the description is minimally adequate. The presence of an output schema means the description doesn't need to explain return values, but it should provide more context about the creation operation's behavior and constraints.
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 'multiple new entities' which hints at the array structure of the 'entities' parameter, but provides no details about what constitutes a valid entity, required fields, or the relationship between entities and observations. With 0% schema description coverage, the description adds minimal value beyond what's implied by the tool name.
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. However, it doesn't explicitly differentiate from sibling tools like 'create_relations' or 'add_observations', which would require more specificity about what constitutes an 'entity' versus other graph elements.
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_relations' or 'add_observations'. It doesn't mention prerequisites, constraints, or typical scenarios for creating entities versus other operations in the knowledge graph ecosystem.
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. While 'Delete' implies a destructive mutation, the description doesn't specify critical details like whether deletions are permanent, require specific permissions, have side effects on connected entities, or include error handling for non-existent relations. This leaves significant gaps 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 states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every part of the sentence contributes directly to understanding what the tool does.
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 that this is a destructive mutation tool with no annotations, the description is minimally adequate but lacks important context. The presence of an output schema helps by documenting return values, but the description doesn't address behavioral aspects like safety, permissions, or error conditions. For a tool that deletes data, more completeness would be expected.
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 schema fully documenting the 'relations' array parameter and its nested properties (from, to, relationType). The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. However, since the schema is comprehensive, a baseline score of 3 is appropriate.
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'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'delete_entities' or 'delete_observations', which would require more specific language about what distinguishes relation deletion from other deletion operations.
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., whether relations must exist), compare with sibling tools like 'delete_entities', or indicate scenarios where this tool is preferred over others. Without such context, the agent must infer usage from the tool name 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks critical details: whether 'open' implies read-only access or modification, what happens if nodes don't exist (e.g., errors or graceful handling), the format of returned data, or any side effects like caching. This is inadequate for a tool with no 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 waste. It front-loads the core action and resource, making it easy to parse. Every word contributes directly to understanding the tool's purpose.
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 (opening nodes by name), the description is minimally complete. It states the purpose but lacks behavioral details. However, the presence of an output schema mitigates the need to explain return values in the description. The gaps in usage guidelines and transparency keep it at an adequate but not fully helpful level.
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 parameter 'names' clearly documented as 'An array of entity names to retrieve'. The description adds minimal value beyond the schema, only implying that nodes are opened by name. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
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'), with the method specified ('by their names'). It distinguishes from siblings like 'read_graph' (which likely reads the entire graph) and 'search_nodes' (which likely searches rather than opens specific nodes). However, it doesn't explicitly contrast with these siblings, 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. It doesn't mention prerequisites (e.g., nodes must exist), exclusions (e.g., not for creating or deleting nodes), or comparisons to siblings like 'read_graph' or 'search_nodes'. The agent must infer usage from the name and context 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 full burden for behavioral disclosure. While 'Search' implies a read-only operation, it doesn't specify whether this requires authentication, has rate limits, returns paginated results, or what happens with empty queries. The description lacks critical operational context that would help an agent use it effectively.
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 search tool and front-loads the essential information. Every word earns its place in conveying the tool's purpose.
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 has an output schema (which handles return values) and 100% schema coverage for its single parameter, the description is minimally complete. However, as a search tool with no annotations and multiple sibling tools that might overlap in function, it should provide more context about when to use it and its behavioral characteristics to be fully helpful.
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 single parameter 'query' well-documented in the schema itself. The description adds no additional parameter semantics beyond what the schema already provides ('The search query to match against entity names, types, and observation content'). This meets 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 action ('Search for nodes') and resource ('in the knowledge graph'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from potential sibling alternatives like 'open_nodes' or 'read_graph', which might also retrieve node information but through different mechanisms.
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 also access node data, there's no indication of when search-based retrieval is preferred over direct access or full-graph reading. No prerequisites, exclusions, or comparative context is mentioned.
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 deletes entities and relations, implying destructive behavior, but lacks critical details like permissions required, whether deletions are reversible, side effects on the graph, or rate limits. This is a significant gap for a destructive 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 a single, efficient sentence with zero waste—it directly states the tool's action and scope. It's appropriately sized and front-loaded, making it easy to parse 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 the tool has an output schema (which covers return values), no annotations, and high schema coverage, the description is minimally adequate. However, for a destructive tool with no annotations, it should provide more behavioral context (e.g., safety warnings, confirmation steps) to be complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the entityNames parameter. The description adds no additional meaning beyond implying deletion includes associated relations, but doesn't clarify parameter semantics like format constraints or examples. Baseline 3 is appropriate as the schema handles documentation.
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 ('Delete multiple entities and their associated relations') and the resource ('from the knowledge graph'), making the purpose specific and unambiguous. It distinguishes this tool from siblings like delete_observations or delete_relations by specifying it targets entities and their relations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for deleting entities and relations together, but provides no explicit guidance on when to use this versus alternatives like delete_relations alone or other deletion tools. No prerequisites, exclusions, or comparative context are mentioned.
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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