NexMem MCP

Server Quality Checklist

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

Latest release: v0.3.0
Disambiguation5/5
Each tool has a clearly distinct purpose: creating vs adding to entities, deleting specific types, reading graph, searching, etc. No overlapping functionality.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (e.g., add_observations, create_entities, read_graph), making it predictable.
Tool Count5/5
11 tools is a well-scoped set for a knowledge graph server, covering CRUD for entities, relations, observations, plus admin operations like status and import.
Completeness4/5
Core operations are present, but missing update operations for entities, observations, and relations (though add_observations and delete_observations allow workarounds).
Average 3.6/5 across 11 of 11 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only says 'open specific nodes' without disclosing read-only status, side effects, permissions required, or what happens to the nodes (e.g., are they returned as data? marked as active?). This is insufficient for a tool that interacts with a knowledge graph.
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 sentence that states the core action and resource. It is concise and front-loaded, but could be slightly improved by adding key constraints 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 lack of annotations, the description does not explain what 'open' returns or modifies. Despite having an output schema (not shown), the description omits expected outcomes. Compared to sibling tools, it is inadequately specified for an operation on graph nodes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage for the `names` parameter. The description adds only 'by their names', which does not specify expected format, uniqueness, case sensitivity, or behavior for missing nodes. With a single required parameter, more detail is needed.
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 uses the verb 'open' and the resource 'nodes in the knowledge graph', and specifies the selection method 'by their names'. This distinguishes it from sibling tools that create, delete, or search nodes, but 'open' could be more precise (e.g., 'retrieve' or 'fetch').
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 alternatives like `search_nodes` or `read_graph`. It does not state prerequisites, limitations, 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 only indicates mutation (add). It does not disclose error handling (e.g., missing entity), idempotency, or safety traits. The output schema exists but isn't shown, so behavioral gaps remain.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: first states purpose, second details parameter structure. No unnecessary words, efficient front-loading of key info.
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 output schema exists, return info is covered elsewhere. However, the description lacks usage guidelines and behavioral details, which are needed for a tool with 0% schema coverage and no annotations. It covers the basics but is not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description adds significant value by specifying that each dict must have entityName (str) and contents (list[str]), which is missing from the schema. This provides critical structure for the agent.
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) and resource (observations to existing entities), and specifies required fields in each dict. However, it does not explicitly distinguish from sibling tools like create_entities or delete_observations, though the purpose is evident.
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 vs alternatives, no prerequisites mentioned (e.g., entities must exist), and no when-not-to-use info. The description implies usage but lacks explicit context for an agent to decide between this and sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It states the destructive action but lacks details on reversibility, safety, atomicity, or permissions. Minimal transparency beyond the core action.
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?
A single, clear sentence with no extraneous information. Front-loaded and efficient.
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 the tool's simplicity and the presence of an output schema, the description is mostly complete. It covers the action and scope, though it could mention side effects or constraints for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not elaborate on the parameter 'entityNames' (format, case-sensitivity, etc.). While the tool description implies the parameter's role, it adds little meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'delete', the resource 'entities', and the scope 'multiple entities and their associated relations'. It distinguishes from sibling tools like delete_relations by explicitly mentioning the deletion of associated relations.
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 delete_relations or create_entities. Absence of context about prerequisites or exclusions reduces usefulness for tool selection.
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?
The description correctly indicates a read-only operation, which is the primary behavioral trait. However, with no annotations provided, the description does not disclose potential performance implications for large graphs or guarantee idempotency. It is transparent about the basic aspect but lacks depth.
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, front-loaded sentence with no waste. It is concise and to the point, but could be slightly expanded to include additional context without breaking conciseness.
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 simplicity (no parameters, output schema exists), the minimal description is adequate but not complete. It does not mention what the output contains (e.g., all entities, relations, observations) or potential data volume. With siblings and no annotations, more context would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and schema coverage is trivially 100%. According to guidelines, 0 parameters yields a baseline of 4. The description adds no parameter information, which is acceptable since there are none to describe.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'read' and the resource 'the entire knowledge graph', which is specific and distinguishes this tool from sibling tools like search_nodes or open_nodes. However, it does not specify the format or structure of the returned graph, leaving minor ambiguity.
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 search_nodes for targeted queries or get_memory_status for summary. The description lacks context for appropriate usage or when not to use it, such as for large graphs that may be slow to retrieve entirely.
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. It implies creation but does not disclose behavior on duplicates, validation, or side effects. Minimal behavioral context beyond the action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no wasted words. First sentence states purpose, second details required structure. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a bulk creation tool with an output schema, the description lacks details on error handling, limits, or return value behavior. Adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% coverage and only specifies an array of objects. The description adds required fields (name, entityType, observations), providing essential meaning missing from the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates multiple new entities in the knowledge graph, specifying the action and resource. It distinguishes from sibling tools like add_observations or create_relations by focusing on entity creation.
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 such as add_observations or create_relations. The description does not mention prerequisites or conditions.
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. Description only details input format, not behavioral traits like destructiveness, permissions, or atomicity. Agent cannot infer side effects 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?
Two succinct sentences: first states purpose, second details parameter structure. No redundant or irrelevant text. Ideal front-loading.
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?
Adequate but incomplete: explains input format but no behavior on success/failure, idempotency, or what happens if observations missing. Output schema exists, partially compensating for missing return value description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description compensates by specifying required keys (entityName, observations) for each dict in the deletions array, adding meaning beyond the permissive schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool deletes specific observations from entities, with a precise verb and resource. It distinguishes from sibling tools like delete_entities, delete_relations, and 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 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 (e.g., delete_entities for deleting entire entities). No exclusions or prerequisites 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 provided, so description carries full burden. Describes expected line format but does not disclose error handling, duplicate behavior, or whether it merges or replaces existing data. 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?
Two succinct sentences. First sentence states purpose, second adds compatibility and format. No redundancy or filler.
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?
One parameter and an output schema exist; description omits return value details and error cases. Adequate for basic use but lacking edge-case context for a bulk import operation.
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?
Only parameter jsonl_content is explained as JSONL with required 'type' field. Adds meaning beyond schema (which only says string), but could detail valid values and structure more thoroughly.
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?
Clearly states it imports entities and relations from JSONL-formatted text. Mentions compatibility with MCP memory exports, distinguishing it from siblings like create_entities (which handle single items).
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?
Implies usage for batch import from compatible exports, but lacks explicit when-not-to-use or alternative tools for individual operations. The context of sibling tools partially compensates.
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 convey behavioral traits. It omits important details such as whether deletion is permanent, if it returns confirmation, or error behavior for missing relations. The description only covers input format, not the consequences of the action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no redundancy. The first sentence states the purpose, and the second clarifies the input format. It is front-loaded and succinct.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a deletion tool with no annotations, the description is incomplete. It does not explain the return value (though output schema exists), error handling, idempotency, or whether the entire graph is affected. The required fields are specified, but additional allowed properties are not mentioned. Overall, the description covers basic usage but leaves gaps in expected behaviors.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage and the items schema is loosely defined with additionalProperties: true. The description adds critical semantic information by specifying the required keys (from, to, relationType) and their types (str), which is not enforced by the schema. This adds significant meaning beyond the schema definition.
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 (relations from the knowledge graph). It specifies that it handles multiple relations, distinguishing it from single-relation operations or other entity operations like 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 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 versus alternatives. The description implies usage for deleting relations but does not mention when not to use (e.g., for single relation deletion or bulk deletion vs iterative calls). Alternatives like create_relations exist but are not referenced.
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 does not disclose side effects, error handling, idempotency, or authorization requirements, leaving significant gaps for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, no wasted words. The first sentence states the purpose, the second adds a clear requirement.
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?
With only one parameter and an existing output schema, the description covers the basics but omits behavioral aspects like failure modes or limitations, leaving it adequate but not comprehensive.
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 description specifies required fields (from, to, relationType) and style (active voice), adding meaningful detail beyond the bare schema (which only defines an array of objects with additionalProperties true).
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 ('Create multiple new relations'), the resource ('relations in the knowledge graph'), and distinguishes from siblings like 'create_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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides a specific guideline ('Relations should be in active voice') but lacks explicit when-to-use or when-not-to-use instructions relative to sibling tools.
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 full burden. It discloses search specifics (matches against names, types, observations) and case-insensitivity, but omits details like result limits, pagination, or error handling. The behavior is generally clear but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, then details. No redundant phrases. Every word contributes.
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 and an output schema, the description covers core behavior. It could mention that results are nodes or hint at result structure, but the output schema presumably handles that. Almost complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With no schema description coverage (0%), the description adds meaningful context: the query is matched case-insensitively against three specific fields. This is essential for correct usage beyond the bare parameter name.
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 that the tool searches for nodes in a knowledge graph based on a query. It specifies the search targets (entity names, types, observation content) and notes case-insensitivity, making it distinct from sibling tools like 'open_nodes' or 'read_graph'.
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 the tool should be used for searching but provides no explicit guidance on when to use it versus alternatives (e.g., 'open_nodes' for retrieving specific nodes). No exclusions 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description implies read-only operation by using 'Show'. Does not explicitly state non-destructive behavior, but output schema exists to clarify returns.
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, directly states purpose and output fields. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, output schema present, and simple status-check function, the description fully covers what the tool does and returns.
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?
No parameters; schema coverage is 100%. Description correctly omits parameter info as none exist, meeting baseline for zero-parameter tools.
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 uses specific verb 'Show' and resource 'memory configuration', listing fields. Clearly distinguishes from sibling mutation tools like add_observations or delete_entities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Clear context for when to use (checking memory configuration). No exclusions or alternatives needed due to simple nature, but no explicit guidance provided either.
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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