Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must disclose behavioral traits on its own. It only states that it adds observations, without detailing whether observations are appended or replaced, what happens if the entity does not exist (e.g., error or auto-creation), or any other side effects. The tool is clearly a write operation, but critical safety and behavior information is missing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence that immediately conveys the tool's core function. There is no fluff or redundant phrasing, and the primary verb and object are front-loaded. It earns a high score for efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there are no annotations and no output schema, the description must provide comprehensive context on its own. However, it only gives a high-level statement and lacks necessary details about input requirements, validation, error handling, or effect on existing data. This leaves significant gaps in the agent's understanding of the tool's full behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has zero coverage for the top-level parameter, and the description does not compensate by explaining the parameter structure. Although the nested schema properties describe entityName and contents, the description adds no semantic value beyond the schema, and the agent must rely solely on the schema to understand that observations is an array of objects with those fields. This is insufficient given the low schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Add' and identifies the resource 'observations' and the target 'existing entities' within the knowledge graph. This clearly distinguishes it from sibling tools like create_entities (which creates entities) and delete_observations (which removes observations), making the tool's purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies that this tool is used when adding observations to existing entities, but it does not explicitly state when to use it over alternatives or provide any comparison with sibling tools. There is no mention of constraints such as 'only for existing entities' or guidance about creating entities first. Thus, the usage context is implied rather than explicitly outlined.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 only states that it creates entities, but does not mention potential duplicate handling, overwrite behavior, validation rules, or whether the operation is atomic—information an agent would need for a mutating tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that communicates the core purpose with no filler. It is appropriately concise for a simple tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of annotations, an output schema, and limited schema coverage, the description should offer more context about usage, side effects, or return behavior. It does not, leaving significant gaps for an agent to make assumptions about how the tool behaves.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema's top-level 'entities' parameter has no description (coverage 0%), and the tool description does not explain what constitutes an entity (name, type, observations). The description adds no value beyond the bare phrase 'multiple new entities,' failing to compensate for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Create') and a specific resource ('multiple new entities in the knowledge graph'), clearly distinguishing this from sibling tools like create_relations and add_observations. It is explicit and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 does not mention create_relations, add_observations, or any exclusions, leaving the agent to infer selection based solely on the name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of behavioral disclosure. It only states 'create' and offers an active-voice guideline, but does not mention idempotency, validation of from/to entities, behavior on duplicates, 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with a brief second clause. Every word earns its place and the main purpose is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no annotations or output schema, the description is too sparse. It omits critical details such as whether from/to entities must already exist, how duplicates are handled, and whether creation is atomic for the batch.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% at the top level, so the description needed to compensate. It adds the active-voice guideline but does not explain the structure of the relations array or the meaning of from/to/relationType, which the schema already partially covers.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (create), the object (multiple new relations), and the context (knowledge graph). It distinguishes from siblings like delete_relations and create_entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this tool is for creating relations, but it does not explicitly state when to use it versus alternatives or mention any prerequisites. The active-voice guideline is a style note, not usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 does disclose that associated relations are deleted as part of the operation, which is a useful behavioral detail. However, it does not mention irreversibility, permissions, or whether observations are affected, leaving gaps in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence that front-loads the action and scope with no unnecessary words. 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.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter destructive tool with no output schema, the description conveys the core purpose but omits behavioral details such as error handling, atomicity, and return values. Given the lack of annotations, it is moderately complete but has room for improvement.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage of the parameter 'entityNames' with a description, so the baseline is 3. The tool description does not add any additional parameter semantics beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool deletes multiple entities and their associated relations, using a specific verb ('Delete') and resource ('entities' and 'relations'). It distinguishes itself from sibling tools like delete_relations, which only handle relations, and delete_observations, which handles observations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 delete_observations. It does not state any exclusions, prerequisites, or scenarios where another tool would be more appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 the action without mentioning permanence, side effects, required permissions, or return format. As 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that directly conveys the core action, with no unnecessary words. It is well-structured and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has a non-trivial parameter schema and no output schema or annotations. The description is too minimal to fully inform usage, lacking details on how to specify deletions and what to expect in response.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description does not explain the 'deletions' parameter structure. Schema coverage is 0% for the top-level parameter, and the description adds no semantic meaning beyond the schema's nested field descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the function: deleting specific observations from entities in a knowledge graph. It distinguishes itself from sibling tools like delete_entities and add_observations by specifying the granularity ('specific observations').

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 such as delete_entities or when not to use it. The description lacks any context or conditions for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must convey behavioral traits, but it only says 'Delete' without mentioning consequences such as irreversibility, partial failure handling, or permissions. For a mutating operation, this is a 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is direct and front-loaded. Every word earns its place, and there is no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple with one well-specified parameter and no output schema, so the core purpose is covered. However, behavioral details like error handling, atomicity, or effects on related entities are absent, which leaves some ambiguity for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema fully describes the 'relations' parameter and its nested properties (from, to, relationType), so the description adds little beyond what is already structured. The phrase 'multiple' aligns with the array type but does not provide extra meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific action ('Delete') on a specific resource ('relations'), and the plural 'multiple relations' distinguishes this from sibling tools like delete_entities and delete_observations. It is concise and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for deleting one or more relations, but it does not explicitly state when to prefer this over alternatives, nor does it mention any exclusions or prerequisites. It provides only minimal contextual guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of disclosing behavior. It only says 'open', which implies read-only retrieval, but does not explicitly state that it is non-mutating, what it returns, or how missing names are handled. This leaves significant ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficiently worded sentence that directly states the action and resource. It contains no filler or redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no output schema and the description does not clarify what 'open' returns (e.g., node attributes, observations, relations). For an agent to invoke the tool and interpret results correctly, this missing information is a notable gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage, already describing 'names' as 'An array of entity names to retrieve'. The tool description merely restates 'by their names', adding no extra semantic detail beyond the schema, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the specific verb 'Open' and identifies the resource 'nodes in the knowledge graph', scoped by 'names', making it clear this is a direct retrieval by exact names. It implicitly differentiates from search_nodes (searching) and read_graph (full graph), but does not explicitly name alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 siblings. The description does not state that it should be used when exact node names are known, nor does it exclude using search_nodes for lookup or read_graph for broader context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It describes the action as 'search' but does not disclose key behaviors such as case sensitivity, partial matching, result limits, ordering, or whether it searches across all entity fields or just specific ones.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no wasted words, but it is vague and lacks structure. It could be improved by adding brief details or examples without increasing length significantly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool without an output schema, the description is minimally adequate. However, given the presence of sibling tools with overlapping functionality, more context (e.g., search scope, result format) would make it complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers 100% of the parameter with a description that explains what the query matches against. The tool description restates 'based on a query' but adds no additional semantics beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches for nodes in the knowledge graph based on a query, which is specific enough to distinguish from siblings like 'traverse_graph' or 'query_by_time'. However, it could be more precise (e.g., specifying it's a full-text search).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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., 'query_by_time', 'traverse_graph', 'read_graph'). No exclusions or context for selection are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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.

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.

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.

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.

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.