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 using the DebuggerAgent, but does not disclose whether the tool executes code, modifies state, or is purely read-only. While 'diagnose' implies analysis, the lack of explicit behavioral details keeps this at a mid-range score.

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 with no filler. It efficiently captures the action, inputs, and expected outcome, making every word earn its place.

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 minimal schema (2 parameters, no output schema) and lack of annotations, the description provides adequate context: it states the input types and the expected result (root cause and fix). It could clarify the output structure or how 'context' should be used, but it is sufficiently complete for its complexity.

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% with descriptions for both 'log' and 'context'. The tool description does not add significant parameter-specific meaning beyond what the schema already provides, so the baseline of 3 applies. The 'log' parameter is implicitly aligned with 'error logs, stack traces, or failing tests'.

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 'Diagnose' and clearly identifies the resources (error logs, stack traces, failing tests) and the intended outcome (root cause and recommended fix). It also names 'DebuggerAgent', which distinguishes it from sibling agent tools like agent_review_code.

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 clear context for when to use the tool: when there are error logs, stack traces, or failing tests to diagnose. It does not explicitly mention alternatives or when not to use, so it misses the highest rating.

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 explaining behavior. It only states that it generates code/implementation, but does not disclose potential side effects (e.g., file modifications), safety profile, or required permissions. This is comparable to a mutation tool with no safety warnings.

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 of 12 words, conveying the purpose without any fluff. It is perfectly concise and front-loaded with the main action ('Generate production code').

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 (agent execution, code generation) and lack of annotations/output schema, the description is insufficient. It does not explain what the output looks like, whether files are written, what prerequisites exist, or how this step relates to the overall agent workflow. The schema covers parameters, but the behavioral context is incomplete.

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. The description does not add extra meaning beyond what schema provides—'step' and 'context' are already described. Per the baseline rule, a score of 3 is appropriate.

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 function: 'Generate production code or step-by-step implementation for a planned step using the ExecutorAgent.' This specifies a concrete verb ('generate'), a clear resource ('planned step'), and the agent type, which distinguishes it from sibling tools like agent_plan_task (planning) and agent_review_code (reviewing).

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 phrase 'for a planned step' implies this tool is used after planning, but it does not explicitly say when to use it versus alternatives such as agent_plan_task or agent_debug_issue. There is no mention of exclusions or conditions for use, providing minimal 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, but the description does not disclose any behavioral traits beyond the core function, such as side effects, required permissions, or output delivery. It only states the action without context on what happens.

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, focused sentence with no wasted words, front-loading the key 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?

The tool has no output schema and no annotations, yet the description does not explain the return format, whether the plan is stored, or any side effects. For a complex planning tool, this is insufficient context.

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 descriptions for both 'task' and 'context' are clear and cover 100% of parameters, so the description adds no additional information. Baseline 3 is appropriate.

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 decomposes complex software engineering requests into structured implementation phases using PlannerAgent, which is a specific verb and resource. It distinguishes from sibling tools like agent_execute_step and agent_review_code by focusing on planning.

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 planning but does not explicitly mention when to use it or alternatives. There is no guidance on when not to use it or prerequisites.

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 disclosure burden. It mentions using the ReviewerAgent but does not state whether the operation is read-only, if code is sent externally, what the response format is, or any side effects. This creates significant ambiguity 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.

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

The description is a single, front-loaded sentence that immediately states the action and scope. Every word contributes value with no filler or redundancy.

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 an agent-based code review with no output schema and no annotations. The description fails to explain what the review returns, how the ReviewerAgent operates, or any prerequisites/limitations, leaving significant gaps for correct invocation.

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 baseline is 3. The description adds no parameter-specific meaning beyond the schema's own descriptions for code and context, relying entirely on the structured schema.

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 it performs a comprehensive code review auditing specific areas (security, performance, quality, best practices) and names the ReviewerAgent. This distinguishes it from siblings like agent_debug_issue and repo_analyze_repository.

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 use for code review but does not explicitly state when to prefer it over alternatives such as repo_analyze_repository or agent_debug_issue. No exclusion or alternative guidance is provided, leaving usage context implicit.

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 discloses that the tool writes and indexes for semantic retrieval, which is useful behavioral context. But it omits other relevant details like return value, deduplication behavior, or any permissions.

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?

Single sentence, front-loaded with the action verb, with no redundant or filler content. Every word adds value.

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 create operation with fully documented parameters, the description is largely complete. However, with no output schema and no mention of return behavior, a small gap remains, but not significant enough to lower the score further.

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?

All four parameters are fully documented in the schema, so the baseline is 3. The description does not add parameter-specific details beyond the schema, though it hints at expected content types (spec, architecture guide) relevant to the category field.

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 specific verbs 'Store and index' and identifies the resource as 'a project document, spec, or architecture guide', with a clear purpose ('for semantic retrieval'). It distinguishes itself from sibling tools like documents_search_documents and documents_list_documents by focusing on the add action.

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 use when you want a document to be stored and semantically retrievable, providing clear context. However, it does not explicitly name alternatives or exclusions, so it stops short of full 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 only says 'Delete a stored document by its UUID,' without disclosing whether deletion is permanent, what happens if the UUID doesn't exist, or any required permissions. For a destructive operation, 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 concise sentence with no unnecessary words or repetition. It gets straight to the point.

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 this is a destructive delete operation with no annotations and no output schema, the description should provide more context about permanence, error handling, or side effects. The simplicity of the tool doesn't excuse the lack of behavioral disclosure. An agent might incorrectly assume the operation is non-destructive or reversible.

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 already provides 100% coverage for the single parameter 'id' with description 'Document UUID to delete.' The tool description's phrase 'by its UUID' adds no new meaning beyond the schema. Baseline 3 is appropriate because 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 action ('Delete'), the target ('a stored document'), and the key identifier ('by its UUID'). This distinguishes it from sibling tools like documents_add_document, documents_search_documents, and documents_list_documents.

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 (delete a document when you have its UUID), but it doesn't explicitly state when to use this tool versus alternatives or provide any exclusionary guidance. There is no mention of when not to use it or that it should be preferred over update/search for deletion purposes.

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. The simple phrase clearly indicates a read-only, list-style operation without side effects, but it does not disclose any additional behavior like pagination, result limits, or scope of 'all'.

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 terse sentence that fully captures the tool's purpose without unnecessary words or repetition.

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 no parameters or output schema, but the description leaves ambiguity about whether 'all' means all project documents across all projects or just the current project, and what 'specs' refers to. Some degree of scope or format would improve completeness.

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 tool takes zero parameters, so the schema provides full coverage. The description does not need to add parameter details; the baseline for zero parameters is 4.

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 specific verb 'List' and the resource 'all stored project documents and specs', making it easy to distinguish from sibling tools like the search, add, or delete document tools.

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 listing all documents, contrasting with search (which filters) but does not explicitly state when to prefer this over alternatives or mention any prerequisites.

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 full burden. It discloses that search is by 'semantic similarity', implying a non-keyword, vector-based search. However, it does not explicitly state that this is a read-only operation, mention rate limits, or describe side effects. The implied read-only nature of 'search' is useful but not fully transparent.

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 states the action and scope without unnecessary words. It is appropriately concise for a simple search 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?

For a simple, read-only search tool with fully described parameters, the description is mostly complete. It clearly identifies the resource and method. However, it does not mention result format or behavior with no results, which would be helpful but is not critical given the tool's simplicity.

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%: all three parameters (query, limit, category) have descriptions. The description adds 'semantic similarity' to explain the query's nature, but this is minimal extra value. Baseline of 3 is appropriate since the schema already documents parameters.

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 verb 'Search' and the resource 'stored project documentation', and specifies the method 'semantic similarity'. This distinguishes it from sibling tools like documents_list_documents (which lists) and documents_add_document (which adds).

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 such as documents_list_documents or memory_search_memory. The description does not mention exclusions, prerequisites, or preferred contexts.

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. It discloses the irreversible nature of the action ('Permanently delete') and the prerequisite that the ID comes from a previous search result. It does not mention permissions or error behavior, but these are less critical for a simple one-parameter delete 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 two sentences, front-loaded with the action, and includes only essential information. No redundant wording; every phrase contributes to understanding the tool's purpose and usage.

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 simplicity (one parameter, no output schema), the description is complete. It specifies what the tool does, what input it needs, and how to obtain that input. It adequately covers all necessary context without over-explaining.

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 already provides 100% coverage for the single 'id' parameter with format and description. The description adds semantic value by clarifying that the ID originates from a previous memory search result, which is useful context not present in the schema.

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 explicitly states the action ('Permanently delete'), the resource ('a stored memory'), and the required identifier ('by its ID'). It clearly distinguishes this from sibling memory tools like memory_save_memory or memory_search_memory by specifying the delete operation.

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 gives a clear usage context by instructing to use the memory ID from a previous search result. This implies the tool is for deleting previously retrieved memories, but it does not explicitly name alternatives or exclusions such as memory_update_memory for modifying instead of deleting.

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 burden of behavioral disclosure. It communicates persistence ('long-term memory', 'future reference') but does not explain what happens on save (e.g., whether duplicates are created, whether an ID is returned, or if there are any size/access constraints). This is adequate but leaves some operational behavior to the agent's inference.

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 two concise sentences, front-loaded with the core action and followed by use-case examples. Every word adds value, and there is no redundant repetition of schema details.

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 save operation with a well-described schema and no output schema, the description provides enough context for an agent to invoke it correctly. It could mention return values or overwrite behavior, but the core purpose, category examples, and resource are sufficiently covered, making it nearly complete for this tool's complexity.

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 covers all parameters with descriptions, so the description adds little param-level detail. However, the prose lists categories that mirror the enum (user preferences, project context, technical decisions, conversations, events), reinforcing the intended values. This earns the baseline score of 3 without exceeding it.

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 ('Save') and clearly identifies the resource ('long-term memory'), and it enumerates the kinds of information to remember (user preferences, project context, technical decisions, conversations, events). This clearly distinguishes it from sibling tools like memory_search_memory, memory_update_memory, and memory_delete_memory.

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 explicitly states when to use the tool ('Use this to remember...') and gives concrete examples of appropriate content. It does not explicitly mention alternatives or exclusions, but the sibling names make the distinction obvious, and the guidance is sufficiently clear for an agent to choose this over search/update/delete.

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 carry the behavioral burden. It usefully discloses that search is by 'semantic similarity' rather than exact keyword, which sets expectations. But it does not cover result ordering, empty-result behavior, or explicitly confirm it is a read-only operation.

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?

Two sentences with no fluff: the first states the core action and method, the second lists concrete usage examples. Every word earns its place.

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 a straightforward search with a fully described schema, and the description provides appropriate use cases and search mechanism. While there is no output schema, the return type (matching memories) is strongly implied by the tool's purpose, so the description is sufficiently 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?

Schema description coverage is 100%, so the baseline is 3. The description reinforces the intent of the query parameter but adds no additional detail about the 'limit' or 'category' parameters beyond what the schema already states.

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 identifies the tool as a semantic search over stored memories with concrete use cases. The verb 'Search' and resource 'memories' distinguish it from sibling memory mutation tools like memory_save_memory and memory_delete_memory.

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 explicit usage scenarios ('recall past conversations, find previously stored project context, retrieve technical decisions, or look up user preferences'), giving clear context for when to use this tool. However, it does not mention alternatives or explicitly state when not to use it, e.g., for keyword-based search or searching documents.

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 discloses one useful behavioral trait: 'The embedding is regenerated automatically.' It does not mention whether metadata is merged or replaced, or any side effects, but it provides some insight beyond the schema.

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 two short sentences that are front-loaded with the core purpose and immediately follow with a key usage note. Every word earns its place; no redundancy or filler.

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 tool with 3 parameters (one nested), no output schema, and no annotations, the description covers purpose, usage, and one behavioral nuance. It is concise yet sufficient for an update operation, though it could elaborate on metadata merging behavior or return value, but that is not strictly required.

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 for all parameters, so the baseline is 3. The description adds minimal value to parameter semantics, only reinforcing that 'content' is the new content and that the ID comes from search. It does not clarify the behavior of the optional 'metadata' parameter beyond the schema.

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 ('Update') with a clear resource ('existing memory') and explicitly distinguishes this from creating a new memory by referencing 'existing' and directing the user to use a memory ID from a previous search. This differentiates it from sibling tools like memory_save_memory or memory_delete_memory.

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 phrase 'Use the memory ID from a previous search result' provides clear context on when to use this tool, implying it is for updating existing memories rather than creating new ones. However, it does not explicitly state when to avoid using it or name alternatives, though the sibling context helps.

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 exist, so the description carries the full burden. It discloses key behaviors: scans all files, performs AST parsing, detects patterns, and generates an AI summary. This goes beyond a minimal description and gives the agent a good sense of what the tool does. It does not mention performance impact or file modification, but the read-only nature of 'analyze' is implied.

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 two sentences, front-loaded with the primary purpose and followed by a concise process summary. Every word earns its place; no redundant or vague language.

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 one simple parameter and no output schema. The description explains its purpose and process, and it explicitly mentions the AI summary as the output. While it does not detail the exact output format, the description is sufficient for an agent to understand what the tool does and what to expect. Slight room for more detail on return structure, but overall 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 100% coverage for the single parameter 'path' with a clear description ('Absolute path to the repository root directory'). The tool description does not add additional parameter semantics beyond the schema, but given full schema coverage, the baseline of 3 is appropriate.

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 verb+resource: 'Analyze a local repository to understand its structure, framework, dependencies, and architecture.' It further details the process (scans all files, AST parsing, detects patterns, generates an AI summary), which distinguishes it from sibling tools like repo_scan_files (just scans) or repo_summarize_repository (just summarizes). This is a comprehensive analysis tool with a clear scope.

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 when a deep understanding of a repository is needed, but it does not explicitly state when to use this tool versus alternatives like repo_explain_architecture or repo_scan_files. There is no mention of exclusions or when to choose a different sibling. The context is clear but not explicit.

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 responsibility for behavioral disclosure. It does mention the use of a generative LLM (Qwen2.5-Coder), which implicitly signals probabilistic, non-deterministic output. However, it does not disclose potential latency, token limits, failure modes, or the need for prior indexing, which are relevant for a retrieval-based 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?

Two tight sentences: the first states the core purpose, the second explains the technical approach. No wasted words, information density is high, and the most important detail (asking a question) comes first.

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 relatively simple tool with 3 params, no output schema, and no annotations, the description covers the essence: what it does and how it works. It could mention the response format or that the answer is generated by an LLM (it does imply that), but the lack of such detail is a minor gap. The prerequisite of an indexed codebase is stated, which is key.

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 provides descriptions for 100% of the parameters, including default and range for maxChunks. The description adds no extra parameter-level detail beyond what the schema already provides, such as the meaning of 'natural language' or how repo_path is matched. Baseline of 3 is appropriate because the schema covers the heavy lifting and the description does not conflict.

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 verb 'Ask' and the resource 'indexed codebase', making its purpose obvious. It also specifies the underlying technology (RAG over ChromaDB, Qwen2.5-Coder), which adds precision. However, it does not explicitly distinguish this tool from sibling repo_* tools like repo_query_knowledge_graph, though the natural language emphasis provides implicit differentiation.

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 phrase 'about an indexed codebase' implies a prerequisite that the codebase must have been indexed, but it does not explicitly say to use repo_index_repository first or explain when to use this tool versus alternatives. The context is clear but no exclusions or alternative guidance is 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?

Without annotations, the description carries the full transparency burden. It states the high-level behavior and scope (files/functions/classes/dependencies) but does not disclose side effects, persistence, permissions, or return behavior. The verb 'Construct' is ambiguous about whether the graph is stored or merely returned.

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 focused sentence with no filler. The key information is front-loaded, and every word contributes to the 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?

Because there is no output schema, the description should explain what the tool returns or how the graph is exposed. It does not, so an agent cannot tell if the result is a graph object, a stored index, or something queryable later. The omission is significant for a tool meant to feed later queries.

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 single parameter path is already fully described in the schema as 'Absolute path to the repository root directory.' The description adds no additional semantic detail, so it gets the baseline score for high 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 opens with a specific verb 'Construct' and identifies the resource: a structural knowledge graph covering files, functions, classes, and dependencies. This differentiates it from siblings like repo_query_knowledge_graph, which implies querying rather than building.

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 explicit guidance is provided about when to prefer this tool over similar repo_* siblings (e.g., repo_index_repository, repo_analyze_repository). The intended use is only implied by the verb 'Construct', so an agent must infer when this is the right choice.

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 present, so the description must fully disclose behavior. It only states the primary action (generating an explanation) and does not mention what the explanation includes, whether it scans files, any side effects, or the nature of the output. This is a significant transparency 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, well-structured sentence that is front-loaded with the core purpose. Every word contributes meaning, with no redundant or wastef ul content.

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 in parameters but lacks an output schema and annotations, and the description does not explain what a 'detailed architectural explanation' entails. It also fails to contextualize how this tool differs from siblings, leaving the agent with insufficient information for confident selection.

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 provides a complete description for the single 'path' parameter, achieving 100% schema coverage. The description adds no additional parameter information, but the high schema coverage justifies the baseline score of 3.

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 generates a detailed architectural explanation of a repository, using a specific verb and resource. However, it does not distinguish itself from sibling tools like repo_explain_project or repo_summarize_repository, so it misses the differentiation required for a 5.

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 about when to use this tool versus alternatives. The description does not mention scenarios, prerequisites, or why one would choose this over repo_explain_project or repo_analyze_repository.

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 behavioral traits. It does not state whether the tool modifies the repository, requires specific permissions, or what the return format is. While 'generate explanation' implies a read-only operation, this is not made explicit.

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 conveys the core purpose and key deliverables. Every word earns its place, with no redundancy or irrelevant detail.

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 simplicity (one parameter, no output schema, no nested objects), the description is largely sufficient. It explains the tool's scope and output content, though it omits explicit mention of the input parameter's role and does not differentiate from siblings, leaving minor gaps.

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 coverage for the single parameter 'path' is 100% with a clear description in the schema. The tool description adds no additional parameter semantics, but per the rubric, a high-coverage baseline of 3 is appropriate even without description-side parameter info.

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 verb ('Generate') and resource ('project explanation') with contents specified (architecture, technology stack, module structure). However, it does not explicitly distinguish itself from sibling tools like repo_explain_architecture or repo_analyze_repository, which may overlap in functionality.

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 over alternatives. There is no mention of prerequisites, exclusions, or alternative tools, leaving the agent without context for selection.

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 carry the full burden of behavioral disclosure. It discloses only the tool's purpose and not how it operates, what it returns, whether it has side effects, or any constraints. For a read-style tool, this lacks important 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, focused sentence with no redundancy or extraneous information. It is well-structured and immediately conveys the core 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?

The tool has no output schema and no annotations, so the description should explain what the result looks like (e.g., file paths, line numbers, or explanations). It also fails to mention whether the repository must be indexed or if there are any limitations. This makes the description incomplete for an agent to confidently invoke the tool.

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 already provides complete descriptions for both parameters ('feature' and 'path') with examples. Since schema coverage is 100%, the description need not add parameter details, so a baseline of 3 is appropriate.

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 ('Locate') and a clear resource ('where a specific feature or capability is implemented in the codebase'). This concisely captures the tool's unique purpose and distinguishes it from sibling tools like repo_analyze_repository or repo_ask_codebase, which have broader or different functions.

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, nor does it mention prerequisites (e.g., whether the repository needs to be indexed). It only states the basic function, leaving the user to infer context from the tool name and description.

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 burden. It indicates an analysis/read-only operation via the word 'Analyze,' but doesn't disclose how impact is computed, whether the repository is indexed, or any prerequisites. It adds the specific scope (file/symbol) and 'potential impact' framing, which is useful but limited.

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 directly states the tool's purpose. It contains no filler, fluff, or redundant information, making it highly concise and effective.

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?

While parameters are fully specified and the purpose is clear, the description lacks any information about the output format or return value. Since there is no output schema, the agent is left without guidance on what the analysis returns, which is a significant gap for a tool of this complexity.

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 already provides descriptions for both parameters (file and path) with 100% coverage. The tool description does not add any parameter-level detail beyond what the schema provides, so the baseline of 3 is appropriate.

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 (analyze), the target (potential impact of modifying a specific file or symbol), and the scope (across the codebase). This distinguishes it from sibling tools like repo_analyze_repository or repo_ask_codebase, which serve different purposes.

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 when to use the tool—when you need to understand the blast radius of a code change—but it does not explicitly state when not to use it or mention alternative tools. The usage context is present but not elaborated.

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 burden of disclosing behavior. It explicitly lists the steps: scans files, splits into chunks, generates embeddings using Ollama, and stores in ChromaDB. It also mentions external dependencies (Ollama, ChromaDB). It does not detail side effects like whether the index is cleared or appended, but the core behavior is transparent.

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, well-structured sentence that front-loads the purpose and then concisely enumerates the processing steps. There is no wasted wording 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?

For a tool with one parameter, no annotations, and no output schema, the description adequately explains what it does, why (semantic search), and how (pipeline with Ollama and ChromaDB). It could mention expected return values or potential performance caveats, but given the simplicity of the input, it is sufficiently 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 fully describes the only parameter (path as 'Absolute path to the repository root directory'), so schema description coverage is 100%. The description does not add further parameter semantics, but none are needed. Baseline 3 is appropriate.

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 function: 'Index a local repository for semantic code search.' It uses a specific verb ('Index') and resource ('local repository'), and goes on to describe the pipeline (scan, chunk, embed, store), which distinguishes it from sibling tools like repo_analyze_repository or repo_scan_files.

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 phrase 'for semantic code search' provides clear context on when to use this tool. It implies this is a prerequisite for querying with repo_ask_codebase, though it does not explicitly name alternatives or state when not to use it. Still, the intended use case is evident.

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 carry full behavioral burden. It fails to mention whether the knowledge graph must be pre-built, whether the operation is read-only, or what the return format is. The only behavioral detail is the query mechanism (by name/type), which is minimal.

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?

A single, front-loaded sentence with no filler or redundancy. It immediately states the action and resource, making it highly scannable and appropriately sized.

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 straightforward query tool, the description is minimally viable but lacks important context such as prerequisites (e.g., the knowledge graph must be built first) and output expectations. Without annotations or an output schema, the description would benefit from additional detail, but it is not entirely inadequate.

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 already provides descriptions for all three parameters (100% coverage), so the baseline is 3. The description adds the entity type list, but this is already present in the enum for the 'type' parameter, so no substantial additional meaning is provided.

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?

Description uses a specific verb 'Query' with a clear resource 'repository knowledge graph' and enumerates entity types (file, function, class, module, dependency). This clearly distinguishes it from siblings like repo_build_knowledge_graph (which builds) and repo_ask_codebase (which likely answers natural language questions).

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 explicit guidance on when to use this tool versus alternatives such as repo_ask_codebase or repo_analyze_repository. The description only states what it does without any contextual hints about prerequisites or preferred scenarios.

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 exist, so the description carries the full burden. It implies a read-only scanning operation but does not disclose whether it respects .gitignore, recursion behavior, performance implications, or any side effects. The phrase 'classified types' is vague and unexplained.

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 communicates the core function efficiently. It avoids unnecessary details, though it could be more informative without being wordy.

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, the description is adequate but lacks key details such as what 'aggregate statistics' include, how file types are classified, and whether scanning is recursive. With no output schema, the description would benefit from more specificity about the return value.

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 coverage is 100% with the 'path' parameter clearly described in the schema. The description does not add additional parameter-level meaning, but given full schema coverage, the baseline of 3 is appropriate.

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 function: scanning a local repository and listing files with type/language classifications and statistics. The verb 'scan' and resource 'local repository' are specific. It does not explicitly name siblings to differentiate, but the output focus on 'list all files' distinguishes it from analysis and indexing tools.

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 like repo_analyze_repository or repo_index_repository. The description only states what it does, not the appropriate context or exclusions.

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 bears full responsibility for disclosing behavioral traits. It only mentions that the summary is 'concise' and 'high-level', which gives minimal insight into output scope. It does not disclose whether the tool reads files, depends on prior indexing, has side effects, or requires permissions. The lack of safety or limitation information makes this 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, focused sentence that immediately states the tool's purpose. There is no redundant information, making it appropriately concise and well-structured.

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 parameter and no output schema, but the description lacks essential context for an agent to use it effectively. It does not explain what the summary contains, how it is generated, or when to prefer it over sibling tools. This is inadequate given the crowded repo_* toolset and absence of usage guidance.

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 already provides a clear description for the single 'path' parameter ('Absolute path to the repository root directory'), covering 100% of parameters. The tool description does not add extra parameter semantics beyond what the schema provides, 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 clearly states the tool's action: 'Get a concise high-level technical summary of the repository.' This specifies the verb ('Get') and resource ('technical summary of the repository'), making the purpose clear. However, it does not differentiate from sibling tools like 'repo_explain_project' or 'repo_explain_architecture', which could also provide summaries, so it lacks sibling differentiation.

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. There is no mention of specific scenarios, prerequisites, or exclusions. Sibling tools like 'repo_analyze_repository' and 'repo_explain_project' are not referenced, leaving the agent without direction on selection.

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