Neo4j Knowledge Graph MCP Server

Server Quality Checklist

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Latest release: v2.7.1
Disambiguation5/5
Each tool targets a distinct operation on entities, observations, or relations. Batch variants are clearly differentiated, and query tools like search_nodes and semantic_search serve different purposes without ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., create_entities, delete_observations, get_relation_history). The '_batch' suffix is uniformly applied for optimized versions, and naming conventions are predictable throughout.
Tool Count5/5
With 21 tools covering CRUD, batch operations, time-travel queries, semantic search, and embeddings, the count is well-scoped for a knowledge graph server. Each tool serves a clear purpose without excess.
Completeness4/5
The tool surface covers core entity, relation, and observation lifecycle management. However, there is no explicit update_observations tool, and batch update for relations is limited to a single update_relation. These are minor gaps in an otherwise comprehensive set.
Average 3.1/5 across 21 of 21 tools scored. Lowest: 2.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 15 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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 must convey all behavioral traits. 'Read' implies a non-destructive operation, but no details about potential cost, size of returned data, or side effects are given. The dummy parameter adds confusion.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short (one sentence), which is concise, but it fails to provide necessary information. It is not a model of effective conciseness as it sacrifices completeness.
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 lack of annotations, output schema, and vague description, the tool definition is incomplete. An agent would not know what data is returned or what 'entire knowledge graph' means. Sibling complexity increases the need for clarity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a dummy parameter clearly described in the schema. The description adds no additional meaning beyond what the schema provides, so 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.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Read the entire knowledge graph system' provides a vague purpose. It uses a verb and resource but does not specify what 'entire knowledge graph system' entails, nor does it distinguish from sibling tools like get_graph_at_time or search_nodes which also read parts of the graph.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus its many siblings (e.g., get_graph_at_time, search_nodes). There is no mention of context or alternatives, leaving the agent to guess.
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 must disclose behavioral traits. It only states creation of multiple relations without detailing side effects, validation, or return behavior. For a mutation tool, 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences and front-loaded, but it sacrifices substance for brevity. It could include more key behavioral context without being verbose.
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 one complex parameter, no output schema, and many sibling tools, the description lacks completeness. It does not explain the structure of relations, required fields, or how it differs from related tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% per context, meaning the description must compensate. However, the description only mentions active voice, adding no semantic value about the relation parameters, their meanings, or constraints beyond what the schema provides.
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 relations) and adds a quality guideline (active voice). However, it does not distinguish from the sibling tool 'create_relations_batch', which likely serves the same purpose.
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 'create_relations_batch' or 'update_relation'. The active voice hint is a quality guideline but does not address usage 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 exist, so the description must disclose behavior. It implies a read operation but does not clarify if the relation must exist, what happens if not found, or any other behavioral traits. This is insufficient for a safe invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, but it omits important details. It is not overly verbose, but the structure could be improved by including usage and behavior information.
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 simple input schema and no output schema, the description is minimally adequate. It lacks context on when to use the tool, how it differs from siblings, and behavioral guarantees. More completeness is needed for effective tool selection.
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 covers all three parameters with clear descriptions. The description adds no further detail beyond 'enhanced properties,' which does not improve parameter understanding. Baseline score of 3 is appropriate given full 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 that the tool retrieves a specific relation with enhanced properties from a knowledge graph. It distinguishes itself from sibling tools like create/delete relations, though 'enhanced properties' is vague.
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 like get_relation_history or search_nodes. The description lacks context for selecting this tool over 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 must carry the full burden of behavioral transparency. It fails to disclose whether the update is a merge or replacement, what happens if the relation does not exist, or if any side effects occur (e.g., cascading updates to related entities). Permissions or authentication needs are not mentioned.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (one short sentence), which is efficient but lacks necessary detail. It is front-loaded with the core action, but would benefit from additional context about how the update works or what output to expect.
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 the tool (nested object with many optional fields, no output schema, multiple sibling tools), the description is incomplete. It does not explain return values, error handling, or how updates affect existing properties. The 'enhanced properties' term is not clarified, leaving ambiguity about which fields can be updated.
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?
Although the tool description adds minimal parameter information, the input schema provides detailed descriptions for each nested property (e.g., 'from', 'to', 'relationType', 'strength'). Since schema description coverage is effectively high due to these property-level descriptions, the description does not need to repeat them, earning a baseline of 3.
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 states the action ('Update') and the resource ('existing relation... in your knowledge graph'), clearly distinguishing it from sibling tools like create_relations or delete_relations. However, the phrase 'enhanced properties' is vague and does not specify what enhancements are possible.
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 like update_entities_batch or create_relations. There is no mention of prerequisites, such as the requirement that the relation must already exist, or that from/to/relationType must uniquely identify a relation.
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?
Without annotations, the description should disclose behavioral traits like idempotency, side effects, or return behavior. Only states 'create' without any behavioral details.
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 clear sentence with no wasted words, but it could include more context without being verbose.
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 no output schema and no annotations, the description lacks details on return values, error handling, or behavior when entities already exist. Incomplete for a creation tool.
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 already provides detailed descriptions for all parameters, so the description adds no additional meaning beyond 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 action (create) and resource (multiple entities), but does not distinguish from the sibling tool 'create_entities_batch', which likely has identical 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?
No guidance on when to use this tool versus alternatives like 'add_observations' or 'create_relations'. No context on prerequisites or when not to use it.
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, the description must fully disclose behavior. It indicates a read operation but fails to mention what the embedding looks like (e.g., vector dimensions, format), any side effects, or required permissions. The behavior is minimally described.
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, clear sentence with no superfluous words. It is front-loaded and efficiently communicates the core action.
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 (no output schema, sibling tools with similar purposes), the description is too minimal. It lacks context about the embedding's content, typical use cases, or how it relates to other tools like semantic_search. The agent would likely need additional information to use it effectively.
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% (the parameter entity_name is described). The description adds no extra meaning beyond the schema, so it earns the baseline score of 3.
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 retrieves a vector embedding for a specific entity from the knowledge graph. It uses a specific verb and resource, and while there are sibling tools like semantic_search that also deal with embeddings, the purpose is distinct enough for an agent to understand the basic action.
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 like semantic_search or get_entity_history. There is no mention of prerequisites, context for usage, or when not to use it, leaving the agent without decision-making support.
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 lacks details on search behavior (e.g., case sensitivity, wildcards, ranking, result limits, pagination). Only basic purpose is stated.
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 clear sentence, front-loaded with the verb and resource. It is concise, though it could potentially add a bit more context without becoming verbose.
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 (3 parameters, no output schema), the description is too minimal. It does not explain return format, behavior with multiple matches, or any constraints. Lacks completeness for an effective AI agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no additional meaning beyond the schema. It does not explain parameter interactions beyond what schema already states.
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 'Search for nodes in your knowledge graph based on a query,' specifying the verb (search) and resource (nodes). It distinguishes from siblings like open_nodes or read_graph by implying text-based search, but does not explicitly contrast with semantic_search or other 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?
The description provides no guidance on when to use this tool versus alternatives (e.g., semantic_search for embeddings, open_nodes for direct navigation). No when-not-to-use or context of use is given.
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 only mentions 'decayed based on time' without disclosing whether the tool is read-only, side effects, performance implications, or output format. Critical behavioral aspects like authentication needs or data transformation are omitted.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise (one short sentence), but it sacrifices necessary details for brevity. It is not verbose, but it is under-specified, lacking context that could be added without making it overly long.
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 absence of output schema and annotations, the description is incomplete. It does not explain the return value, how decay works, or how it differs from similar tools like 'get_graph_at_time'. The tool's complexity (two optional parameters, no required fields) demands more 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 coverage is 100% and parameters have descriptions in the input schema. The tool description adds no extra meaning beyond what the schema already provides, so it meets the baseline but does not enhance parameter understanding.
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 gets the knowledge graph with confidence values decayed based on time. The verb 'Get', resource 'knowledge graph', and specific aspect 'decayed based on time' provide a specific and unique purpose, distinguishing it from siblings like 'read_graph' or 'get_graph_at_time'.
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 vs alternatives, no exclusions, and no context on prerequisites or preferred scenarios. It only states what the tool does, leaving the agent to infer usage without comparison to 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?
No annotations provided, so description must fully disclose behavioral traits. It only states 'Add new observations' without specifying whether existing observations are overwritten, if duplicates are allowed, or any side effects (e.g., requiring entity existence). Lacks key mutation details.
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?
Single, clear sentence. It is concise but could include brief usage hints without sacrificing 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?
Without output schema and with many sibling tools, the description is minimal. Fails to explain what an observation is, how it relates to entities, or any constraints (e.g., limit on contents array size).
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 75% (high), so baseline is 3. The description adds little beyond the schema—just restates the tool's purpose. Does not clarify defaults or interaction between per-item and global parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Add', resource 'observations', and target 'existing entities in your knowledge graph'. It effectively distinguishes from siblings like 'add_observations_batch' and other entity/relation tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus the batch variant 'add_observations_batch'. No when-not-to-use or alternative suggestions 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?
The description only states 'delete', implying a destructive action, but with no annotations to reinforce safety. It does not disclose reversibility, cascading effects, required permissions, or any side effects, failing to meet the burden for an unannotated 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 sentence, front-loaded with the action and object. It contains no fluff, and every word is necessary to convey the core 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 simplicity of the tool (one parameter, no output schema, no annotations), the description is minimal but barely adequate. It lacks any behavioral context like return values or confirmation, leaving some gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with all properties described in the schema. The description adds no additional meaning beyond what the schema already provides, such as clarifying the 'relations' array or its items. Baseline 3 is appropriate as no extra value is added.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it deletes multiple relations, using a specific verb and resource. It distinguishes from sibling tools like 'delete_entities' or 'delete_observations' by specifying 'relations', though it does not further differentiate from 'create_relations' or 'update_relation' which is implied by the action.
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. There are no explicit context hints, usage caveats, or mentions of when not to use it, leaving the agent without decision support.
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, and the description offers minimal behavioral context. It does not disclose what 'version history' entails (e.g., chronological order, change details), potential limits, or authentication requirements. For a read operation, more transparency is needed.
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, concise sentence with no wasted words. However, it may be too brief; slightly more detail (e.g., output format) could improve usefulness without sacrificing conciseness.
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 history retrieval tool with no output schema and 18 sibling tools, the description is incomplete. It fails to explain response format, ordering, or limitations, leaving the agent with insufficient context for correct 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?
Schema description coverage is 100% with clear parameter descriptions. The tool description adds no additional meaning beyond the schema, achieving the baseline score of 3 as per rubric.
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 'Get the version history of a relation from your knowledge graph' clearly states the action (get history) and resource (relation), distinguishing it from siblings like 'get_relation' (current state) and 'get_entity_history' (entity focus).
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 (e.g., get_relation, get_graph_at_time). It implicitly suggests history retrieval context but lacks explicit when-to-use or when-not-to-use instructions.
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 must disclose behavioral traits. It only states it performs semantic search, but doesn't mention idempotency, read-only nature, performance considerations, or how hybrid search behaves. The schema describes parameters like hybrid_search and semantic_weight, but the tool description doesn't explain their impact on behavior.
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 concise sentence that directly states the tool's function. It could be slightly more structured (e.g., listing key features), but it's not verbose.
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 8 parameters and no output schema, the description is too sparse. It doesn't explain what results are returned, how to use parameters like min_similarity or hybrid_search in practice, or any context for interpreting similarity scores. The tool description relies heavily on the schema descriptions for detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description does not add any additional semantics beyond the schema; it's a general statement that doesn't elaborate on parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: searching for entities semantically using vector embeddings and similarity in the knowledge graph. It uses specific verb+resource and distinguishes from sibling tools like search_nodes (likely keyword-based) and read_graph (returns all 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, such as keyword search or graph traversal. No context on prerequisites or situations where semantic search is not 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 provided, so description must disclose behavioral traits. It only states deletion occurs but omits details on permanence, side effects, permissions, or reversibility. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence is concise and front-loaded. Efficient but could include more detail without becoming verbose.
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 1 nested parameter, no output schema, and 19 siblings, the description lacks return behavior, error handling, and usage context. Incomplete for effective agent 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%, but description adds no additional parameter meaning beyond the schema structure. Fails to compensate for missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'delete', resource 'observations', and context 'from entities in your knowledge graph'. Effectively distinguishes from siblings like delete_entities and delete_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?
No explicit guidance on when to use or avoid this tool. While the name implies it's for deleting observations, no mention of alternatives or prerequisites. Moderate adequacy.
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, the description carries full burden for behavioral traits, but it only says 'Get the version history' without detailing side effects, return format, or potential limitations. This is insufficient for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no wasted words. It efficiently conveys the core 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?
Despite low complexity, the description omits crucial context like output structure (e.g., list of changes with timestamps), scope of history, and any constraints. With no output schema, this information should be in the description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add any meaning beyond what the schema already provides for the single parameter. It merely restates the tool's purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the specific resource 'version history of an entity', effectively distinguishing it from sibling tools like get_relation_history. The purpose is unambiguous.
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 (e.g., get_graph_at_time, get_relation_history). The description does not mention usage context, prerequisites, or when not to use it.
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, the description should disclose behavioral traits. It mentions speed but not error handling, partial failure, permissions, or whether operations are transactional—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?
Single sentence with no wasted words. Front-loaded with purpose and key benefit. Highly concise.
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?
No output schema, no annotations, and the description omits return value, error handling, or any guidance on batch behavior. Incomplete for a nested-parameter batch tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline 3. The description adds no specific meaning beyond schema; it only repeats that it's a batch operation. Schema already describes parameters sufficiently.
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 updates multiple entities in a batch, with a performance claim. However, it doesn't explicitly mention that updates can include adding/removing observations, which is part of the schema.
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?
It implies use for batch updates over individual ones due to speed, but lacks explicit when-to-use or alternatives, and doesn't discuss trade-offs like atomicity or comparison with sibling batch 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?
No annotations are provided, so the description carries the full burden. It only mentions performance (optimized, 10-50x faster) but does not disclose any behavioral traits such as atomicity, error handling, failure modes, or rate limits.
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 sentence with no wasted words, efficiently conveying the purpose and key benefit.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks essential context: no mention of return values, error handling, or limitations. For a batch tool, users need to know about batch size limits and non-atomicity, which are not covered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents both parameters. The description adds no additional meaning beyond the schema, hence baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'create', the resource 'multiple entities', and highlights the speed advantage (10-50x faster). It distinguishes from sibling tools like 'create_entities' which is for single entities.
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 use for batch creation and mentions performance benefits, but it does not explicitly state when to avoid this tool (e.g., for single entity creation) or mention alternative tools like 'create_entities'.
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 must bear the full burden. 'Open' is ambiguous—does it retrieve, navigate, or modify? The description lacks behavioral context such as whether it's read-only or requires permissions.
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?
Single sentence of 10 words, no waste, front-loaded with verb and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (1 param, no output schema), but the ambiguous 'open' leaves gaps. Adequate but not fully informative for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the 'names' parameter. The description adds minimal value beyond restating 'by their names'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'open' and resource 'nodes in your knowledge graph', specifying the action by names. It distinguishes from siblings like 'create_entities' 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?
No guidance on when to use this tool vs alternatives like 'read_graph' or 'search_nodes'. The term 'open' is ambiguous and no exclusions or prerequisites are given.
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, the description must disclose behavioral traits. It only states the basic read operation (getting a historical snapshot) but does not mention side effects, rate limits, permissions, or data completeness (e.g., whether all entities/relations are returned). The description is minimal and does not add value beyond the purpose.
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 sentence that is front-loaded and contains no filler. Every word is essential and directly conveys the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single parameter with full schema description and no output schema, the description adequately conveys the tool's function. However, it could be more specific about the output format (e.g., returns a graph object with entities and relations). The simplicity of the tool makes the description mostly 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?
The schema covers 100% of parameters, with the 'timestamp' parameter described clearly as milliseconds since epoch. The description adds no additional meaning beyond the schema's parameter documentation. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves the knowledge graph as it existed at a specific point in time. The verb 'Get' and resource 'knowledge graph' with a temporal qualifier make the purpose unambiguous, and it distinguishes from sibling tools like 'read_graph' (current graph).
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 'read_graph' or 'get_decayed_graph'. The description implies it is for historical queries but does not state when not to use it or provide context for selecting 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 bears full responsibility for behavioral disclosure. It only mentions performance and batch processing, but fails to disclose important traits like error handling (partial vs atomic failure), idempotency, side effects, or rate limits. Critical gaps for a batch 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, well-structured sentence that front-loads action and benefit. Every word adds value, with no unnecessary verbosity.
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?
The tool has complex nested parameters, no output schema, and no annotations. The description omits return value format, error semantics, and batch atomicity. For a batch mutation tool, this leaves agents with insufficient context to handle failures or interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the input schema already fully documents parameters. The description adds no extra parameter-level detail; the performance hint is a general attribute, not parameter-specific. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Add observations' and the resource 'multiple entities'. It also highlights the batch nature and performance advantage (10-50x faster), effectively distinguishing from the sibling tool 'add_observations'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly mentions the batch context and performance benefit, implying use when adding multiple observations. However, it does not explicitly state when not to use it or suggest alternatives like 'add_observations' for single additions.
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 must disclose behavioral traits. It only mentions speed optimization but omits critical details like atomicity, partial failure handling, duplicate behavior, authentication requirements, or side effects. For a batch mutation tool, 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 a single sentence that front-loads the core purpose and key benefit. Every word adds value; no redundancy or unnecessary details.
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 operation and the lack of an output schema, the description fails to explain return values, error handling, limitations on number of relations, or how to interpret results. This is inadequate 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 coverage is 100%, and the description adds context about the batch optimization but does not enhance understanding of individual parameters beyond what the schema descriptions already provide. Baseline score is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'create' and resource 'multiple relations', and highlights the optimized batch aspect with performance benefit (10-50x faster), clearly distinguishing it from the sibling 'create_relations' tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates this tool is for batch creation when performance is needed, implying it should be used over individual creates. However, it does not explicitly state when not to use it or provide alternative tool names for different scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions deleting associated relations, which adds behavioral context, but lacks details on side effects, permissions, or error handling.
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, concise sentence that is front-loaded with the verb and resource, with no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description adequately covers the core functionality, though it could mention error cases or behavior for non-existent entities.
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% coverage for the single parameter, and the description does not add additional meaning beyond what the schema already provides.
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) and the resource (multiple entities and their associated relations), distinguishing it from siblings like delete_observations or delete_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 but provides no explicit guidance on when to use this tool versus alternatives like delete_relations or read_graph.
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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