RAG MCP Server

by ali-48

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
Most tools have distinct purposes centered around knowledge graph operations, but there is some overlap between 'search_nodes' and 'search_code' that could cause confusion. Additionally, 'injection_rag' and 'update_project' both involve RAG indexing but differ in scope, which might lead to misselection if not carefully described.
Naming Consistency2/5
The naming is inconsistent with a mix of snake_case English verbs (e.g., 'add_observations', 'create_entities') and French terms (e.g., 'injection_rag', 'manage_projects'), and some tools use different verb styles like 'open_nodes' vs. 'read_graph'. This lack of a predictable pattern reduces clarity and usability.
Tool Count4/5
With 13 tools, the count is reasonable for a RAG and knowledge graph server, covering core operations like CRUD for entities, relations, and observations, as well as project management and search. It's slightly on the higher side but still well-scoped for the domain.
Completeness4/5
The tool set provides good coverage for knowledge graph management and RAG operations, including create, read, update, and delete functions for entities, relations, and observations. Minor gaps exist, such as no explicit tool for updating entities or relations, but agents can likely work around this using deletion and recreation.
Average 2.9/5 across 13 of 13 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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{
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  ]
}
```
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool can 'manage' projects, implying potential write operations, but doesn't specify what management entails (e.g., creation, deletion, modification) or any behavioral traits like permissions, side effects, or response format. This leaves significant gaps in understanding how the tool behaves beyond basic parameter handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient French phrase ('Gérer et lister les projets indexés') that is front-loaded and wastes no words. It could be slightly more structured by separating the two functions, but it remains appropriately concise without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (managing and listing projects with two parameters) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'manage' involves, how results are returned, or any prerequisites, leaving the agent with insufficient context to use the tool effectively beyond basic parameter passing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting both parameters with enums and defaults. The description adds no additional meaning beyond the schema, such as explaining the context of 'indexed projects' or detailing the 'stats' action. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but no extra value is contributed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Gérer et lister les projets indexés' states the tool manages and lists indexed projects, which provides a general purpose but lacks specificity. It mentions two actions (manage and list) but doesn't clarify what 'manage' entails or how it differs from sibling tools like 'update_project'. The purpose is somewhat vague rather than clearly distinguishing this tool's unique function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't indicate scenarios for choosing 'manage_projects' over sibling tools such as 'update_project' for modifications or 'read_graph' for data retrieval. Without any context on usage timing or exclusions, the agent must infer from the tool name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a creation operation, implying mutation, but doesn't cover critical aspects like permissions needed, whether creation is idempotent, error handling for duplicates, or rate limits. For a batch creation tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action ('create multiple new entities') and specifies the context ('in the knowledge graph'), making it easy to parse quickly. Every word earns its place, with no redundancy or fluff.
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 batch creation in a knowledge graph, no annotations, and no output schema, the description is insufficient. It doesn't address return values, error conditions, or interactions with sibling tools like 'read_graph' or 'delete_entities'. For a mutation tool with multiple parameters and siblings, more context is needed to ensure proper usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the schema provides no descriptions for parameters. The description mentions 'multiple new entities' but doesn't explain what 'entities' entail beyond the schema's structure. It fails to add meaningful context about the 'entities' array, such as format examples, constraints on entity types, or how observations are used, leaving parameters largely undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('create') and resource ('multiple new entities in the knowledge graph'), making the purpose unambiguous. It distinguishes from siblings like 'create_relations' or 'add_observations' by focusing on entity creation rather than relationships or observations. However, it doesn't specify what constitutes an 'entity' beyond the schema, leaving some 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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as whether entities must be unique or if there are limits on batch size. With siblings like 'create_relations' and 'add_observations', there's no indication of how this tool interacts with them or when to choose one over another.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool adds observations to existing entities, implying a mutation operation, but lacks details on permissions, side effects (e.g., if duplicates are allowed), error handling, or response format. This is inadequate for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and appropriately sized, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a mutation operation with nested parameters), lack of annotations, and no output schema, the description is incomplete. It fails to address critical aspects like behavioral traits, error conditions, or return values, making it insufficient for safe and effective use by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description mentions 'observations' and 'existing entities,' which aligns with the input schema's 'observations' array containing 'entityName' and 'contents.' However, it doesn't add meaningful semantics beyond this basic mapping, such as format constraints or examples, leaving parameters partially documented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Add new observations') and target ('to existing entities in the knowledge graph'), which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'create_entities' or 'delete_observations' beyond the implied distinction between adding to existing entities versus creating new ones.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., entities must exist), exclusions, or comparisons to siblings like 'create_entities' for new entities or 'delete_observations' for removal, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a creation operation, implying mutation, but doesn't disclose critical traits like required permissions, whether it's idempotent, error handling, or rate limits. The active voice hint adds minor context but doesn't compensate for the lack of behavioral details, making it inadequate for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences that directly state the tool's function and a stylistic requirement. Every word earns its place, and it's front-loaded with the core purpose. There's no redundancy or unnecessary elaboration, making it efficient and well-structured.
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 (a mutation tool creating relations in a knowledge graph), lack of annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't explain return values, error cases, or behavioral nuances, leaving the agent under-informed. The active voice hint is insufficient to cover these gaps, making it 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 description coverage is 0%, so the schema provides no parameter descriptions. The description adds minimal semantics by implying 'relations' is an array of objects with 'from,' 'to,' and 'relationType,' but doesn't explain what these mean beyond the schema's structure. It doesn't clarify data formats, constraints, or examples, leaving significant gaps. Baseline 3 is appropriate as it adds some value but doesn't fully compensate for the 0% coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('create') and resource ('multiple new relations between entities in the knowledge graph'), making the purpose understandable. It distinguishes from siblings like 'create_entities' (which creates entities, not relations) and 'delete_relations' (which removes relations). However, it doesn't explicitly differentiate from all siblings (e.g., 'add_observations' might be similar in some contexts), so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions 'relations should be in active voice,' which is a stylistic hint but not a usage guideline. There's no mention of prerequisites, when not to use it, or comparisons to siblings like 'create_entities' or 'delete_relations,' leaving the agent with minimal context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it correctly indicates this is a destructive operation ('Delete'), it doesn't address critical behavioral aspects like whether deletions are permanent/reversible, what happens to orphaned data, permission requirements, rate limits, or error handling for invalid entity names.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized for a tool with one parameter and gets straight to the point without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'associated relations' means operationally, what confirmation or validation occurs, what the return value or success indicators are, or how errors are handled - critical gaps for a deletion 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?
Schema description coverage is 100%, with the single parameter 'entityNames' well-documented in the schema. The description adds no additional parameter semantics beyond what the schema already provides about the array of entity names to delete, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Delete') and target resources ('multiple entities and their associated relations from the knowledge graph'), providing a specific verb+resource combination. However, it doesn't explicitly distinguish this tool from sibling tools like 'delete_observations' or 'delete_relations', which handle different resource types in the same system.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'delete_observations' or 'delete_relations', nor does it mention prerequisites, constraints, or appropriate contexts for invocation. It simply states what the tool does without 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?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states this is a deletion operation (implying destructive), but doesn't mention permissions needed, whether deletions are permanent/reversible, rate limits, or what happens to related data. For a destructive tool with zero annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized for a basic tool description and gets straight to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no annotations, no output schema, and 0% schema description coverage, the description is insufficient. It doesn't explain what 'observations' are, how they relate to entities, what the deletion entails, or what the agent should expect as a result. More context is needed given the complexity and risk profile.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the schema provides no parameter documentation. The description mentions 'specific observations' and 'entities in the knowledge graph', which hints at the parameters but doesn't explain what entityName represents, what format observations take, or how deletions are processed. It adds marginal context but doesn't fully compensate for the coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Delete') and target ('specific observations from entities in the knowledge graph'), which distinguishes it from sibling tools like delete_entities or delete_relations. However, it doesn't specify what 'observations' are or how they differ from other graph elements, leaving some 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 delete_entities or delete_relations. The description implies it's for removing observations, but doesn't clarify prerequisites, dependencies, or scenarios where this is the appropriate choice among deletion tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Delete' implies a destructive mutation, it doesn't specify whether this operation is reversible, what permissions are required, how errors are handled, or what happens to related data. For a destructive tool with zero annotation coverage, this is a significant gap in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every word earning its place in conveying the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after deletion, whether there are side effects, what the return value looks like, or any error conditions. Given the complexity and risk of a delete operation, more contextual information is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the schema fully documenting the 'relations' array parameter and its nested properties. The description adds no additional parameter semantics beyond what's already in the schema, so it meets the baseline score of 3 where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Delete') and resource ('multiple relations from the knowledge graph'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'delete_entities' or 'delete_observations' beyond specifying 'relations' as the target resource.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'delete_entities' or 'delete_observations'. It lacks context about prerequisites, when-not-to-use scenarios, or explicit comparisons with sibling tools, leaving the agent to infer usage from tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions analyzing, preparing, and injecting data automatically, but doesn't specify what 'injecte' entails (e.g., where data is stored, if it's destructive, authentication needs, or rate limits). The phrase 'automatiquement' hints at automation but lacks operational 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, efficient sentence that front-loads the core action. It could be slightly more structured (e.g., separating analysis from injection phases), but it avoids redundancy and wastes no words.
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 complex tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what 'RAG' means, what data is injected where, or the expected outcomes. Given the lack of structured fields, more behavioral and contextual details are needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 5 parameters. The description adds no parameter-specific information beyond what's in the schema, such as explaining how 'file_patterns' relate to analysis or what 'enable_graph_integration' does in practice. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyse du projet complet + prépare et injecte les données automatiquement (Phase 0 → RAG)'. It specifies the verb (analyze, prepare, inject) and resource (project data), though it doesn't explicitly differentiate from sibling tools like 'manage_projects' or 'update_project'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions 'Phase 0 → RAG' but doesn't explain what this phase entails or how it relates to other tools like 'search_code' or 'read_graph'. No exclusions or prerequisites are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states 'open' but doesn't clarify what 'open' means operationally—whether it retrieves node details, expands them in a UI, or performs some other action. It doesn't disclose permissions needed, rate limits, or what happens if nodes don't exist. For a tool with no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the core action and resource, making it easy to scan. Every word contributes to understanding the tool's 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?
Given no annotations and no output schema, the description is incomplete for a tool that likely retrieves or accesses node data. It doesn't explain what 'open' entails behaviorally, what information is returned, or how errors are handled. For a knowledge graph tool with siblings that include mutations, more context is needed to ensure safe and correct use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'names' fully documented in the schema as 'An array of entity names to retrieve'. The description adds minimal value beyond this, only implying that names are used to identify nodes. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('open') and target resource ('specific nodes in the knowledge graph'), specifying they are opened 'by their names'. It distinguishes from siblings like 'search_nodes' (searching) and 'read_graph' (reading entire graph). However, it doesn't explicitly differentiate from 'create_entities' or 'delete_entities' in terms of effect on nodes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'search_nodes' (for finding nodes) or 'read_graph' (for broader graph access). It mentions opening nodes 'by their names', which implies you need to know exact names, but doesn't state this as an explicit prerequisite or exclusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'read' implies a safe operation, but doesn't clarify performance aspects (e.g., is this a heavy operation that might time out?), data format (structured graph? raw text?), or side effects (does it cache data?). For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
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 wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word contributes to understanding the tool's function.
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 knowledge graph tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'read' returns (e.g., nodes, edges, metadata) or handle potential issues like large graph sizes. For a tool that likely interacts with structured data, more context on output and behavior is needed to be 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?
The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't mention any parameters, which is appropriate since none exist. It implies the tool reads the graph without inputs, adding value by clarifying the scope ('entire'), though it could specify if filters or options are implicitly applied.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Read the entire knowledge graph' clearly states the action (read) and resource (knowledge graph), making the purpose understandable. However, it lacks specificity about what 'entire' means (all nodes/relations? all data without filtering?) and doesn't distinguish this from sibling tools like 'search_nodes' or 'open_nodes', which might also read graph data with different scopes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'search_nodes' (likely for filtered queries) and 'open_nodes' (possibly for specific nodes), there's no indication that this tool is for bulk retrieval versus targeted access. No prerequisites, exclusions, or comparative context are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 mentions 'semantic search' and 'RAG options' but doesn't disclose critical behavioral traits: whether this is read-only or has side effects, what the output format looks like, if there are rate limits, authentication needs, or how results are returned (e.g., pagination). The description is too vague to inform safe and effective use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence in French that directly states the tool's purpose. It's appropriately sized and front-loaded with key information. However, it could be slightly more structured by separating the core function from the RAG feature.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a search tool with RAG options, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, and usage guidelines. For a tool that likely returns complex results, more context is needed to ensure the agent can interpret and use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters ('query' and 'project_filter') with descriptions. The tool description adds no additional meaning beyond what's in the schema—it doesn't explain parameter interactions, default behaviors, or examples. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs 'semantic search in indexed code with RAG options', which specifies the verb (search), resource (indexed code), and method (semantic with RAG). It distinguishes from siblings like 'search_nodes' by focusing on code rather than general nodes. However, it doesn't explicitly differentiate from other search tools beyond the resource type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'search_nodes' or 'injection_rag'. It mentions RAG options but doesn't explain when semantic search with RAG is preferred over other methods. No exclusions, prerequisites, or contextual advice 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 provided, the description carries full burden for behavioral disclosure. It states the basic search functionality but doesn't describe what 'search' entails - whether it returns partial matches, supports advanced operators, has pagination, returns specific fields, or has any rate limits or authentication requirements. For a search tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for a single-parameter search tool and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with no annotations and no output schema, the description is incomplete. It doesn't explain what constitutes a 'node' in this context, what fields are searched (beyond what's implied in the schema), what the return format looks like, or how results are structured. The agent would need to guess about the tool's behavior and outputs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the single 'query' parameter with its description. The tool description adds no additional parameter information beyond what's in the schema, so it meets the baseline for adequate but unenriched parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Search for nodes in the knowledge graph based on a query', specifying the verb (search), resource (nodes), and context (knowledge graph). It distinguishes from obvious non-search siblings like create/delete operations, but doesn't explicitly differentiate from other search tools like search_code.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when search_nodes is appropriate versus open_nodes, read_graph, or search_code, nor does it specify any prerequisites or contextual constraints for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. 'Mettre à jour' implies a mutation operation, but it doesn't disclose whether this requires specific permissions, what 'indexation incrémentale' entails practically, how long it might take, whether it's idempotent, or what happens on failure. The mention of RAG options is vague without explaining their impact.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core purpose. There's no fluff or redundancy. However, it could be more front-loaded with critical behavioral context given the mutation nature and lack of annotations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with 7 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what 'indexation incrémentale' means operationally, what RAG options do, what the tool returns, or error conditions. The agent lacks sufficient context to use this tool confidently without trial and error.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 7 parameters. The description adds no parameter-specific information beyond what's in the schema—it doesn't explain relationships between parameters (e.g., how 'embedding_provider' interacts with 'embedding_model') or provide usage examples. Baseline 3 is appropriate when the schema does all the work.
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 ('Mettre à jour l'indexation d'un projet') and specifies it's incremental indexing with RAG options. It distinguishes from siblings like 'manage_projects' or 'search_code' by focusing on indexing rather than general management or search. However, it doesn't explicitly differentiate from potential similar indexing tools if they existed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, when incremental indexing is appropriate, or how it differs from other project-related tools like 'manage_projects' or 'injection_rag'. The agent must infer usage from the name and parameters alone.
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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