Smart-AI-Bridge

Server Quality Checklist

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

Latest release: v2.8.1
Disambiguation4/5
Most tools have clearly distinct purposes, with descriptions explicitly contrasting related tools (e.g., review vs analyze_file vs council). A few pairs like generate_file and dual_iterate are conceptually close, but the descriptions clearly differentiate single-shot vs iterative generation. Overall, an agent can reliably choose the right tool.
Naming Consistency3/5
The naming is a mix of bare verbs (ask, explore, refactor), verb_noun patterns (spawn_subagent, analyze_file), and atypical patterns (council, parallel_agents, backup_restore). All names are snake_case and readable, but the convention is not uniform across the set.
Tool Count4/5
With 17 tools, the server sits slightly above the ideal 3-15 range, but each tool earns its place given the broad scope of LLM querying, multi-agent workflows, file operations, analysis, backup, and monitoring. No tool feels redundant, though the count is a touch high.
Completeness4/5
The server covers almost the full lifecycle of AI-assisted coding: create (generate_file), edit (modify_file), batch edit (batch_modify), refactor, analyze (analyze_file, batch_analyze, explore), review, multi-agent collaboration (spawn_subagent, parallel_agents, council), and even backup/restore and monitoring. The only notable gap is a direct file deletion tool, which is outside the core AI-bridge domain.
Average 4.7/5 across 17 of 17 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 41 commits in the last 12 weeks
- Last stable release on August 7, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it discloses the operation is read-only, makes exactly one HTTP request, and lists the exact return fields including success/error and latency. It does not mention caching behavior, which is relevant given the 'force' parameter, but the core behavioral traits are transparently covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with the action, and each sentence provides distinct information—scope, read-only nature, and return format—without redundancy. Every word earns its place, making it a model of concise tool documentation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only health check with no output schema, the description sufficiently documents the return payload and the tool's scope. It could mention caching behavior to fully align with the 'force' parameter, but overall it is complete enough for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% coverage with descriptions for both parameters (backend and force), so the description does not need to compensate. It adds no additional parameter-specific meaning beyond what the schema already states, 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'ping' and identifies the resource as 'one specific backend's API endpoint', explicitly contrasting with 'not the whole fleet'. This clearly distinguishes the tool from any fleet-wide check, giving a precise statement of what it does and its scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides clear context that this tool is for checking a single named backend and notes it is not a fleet-wide check, which helps the agent decide when to use it. However, it does not name an alternative tool for fleet-wide checks or other scenarios, so it stops short of explicit when-not/alternatives guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses key behavioral traits: 'this tool does not read any file from disk' and 'Read-only: never writes to disk.' It also outlines the return structure. A small gap is that it doesn't mention potential rate limits or token usage, but the critical safety aspects are covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is longer than a single sentence but every clause earns its place: it covers purpose, usage context, alternatives, read-only behavior, and return format. It is well-structured and front-loaded, without redundant filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple with 4 parameters and no output schema. The description supplies the return shape, emphasizes the content requirement, and names alternatives. It sufficiently equips the agent to select and invoke the tool correctly, though additional details about output size or review depth would push it to a 5.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers 3 of 4 parameters (75% coverage), so the baseline is near-average. The description adds emphasis on passing code in `content` but does not elaborate on language or review_type beyond what the schema's enum provides. It provides only marginal extra semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb and resource: 'Review a code blob you already have in context and return structured findings + a quality score + improvement suggestions.' It distinguishes itself from siblings by explicitly naming analyze_file and council as alternatives for different use cases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit when-to-use guidance ('Use when Claude already has the code in hand'), when-not-to-use (for files not seen, use analyze_file), and when to use an alternative (council for multiple perspectives). It also clarifies what to pass in the content parameter.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility. It discloses that the tool is read-only ('never writes'), walks the filesystem, reads matched files, and returns a summary along with file:line matches capped at 15. It also notes the use of LLM summarization for deep depth. This is strong transparency, though it stops short of discussing performance or rate limits, which are less critical for a read-only search.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is six sentences, each contributing distinct value: purpose, output type, usage trigger, alternatives, depth semantics, and return shape/safety. It is front-loaded with the core purpose and contains no filler or redundant restatements of the tool name or schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, so the description's full return object specification (including fields like evidence with a cap of 15) is essential and well-covered. It includes safety behavior, usage scenarios, and parameter semantics. For a search tool with nested options, this description is thorough and leaves no major gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning to the 'depth' parameter (shallow = fast grep, deep = LLM context) and clarifies that 'question' is natural-language. It also references glob patterns within the tool's domain. However, it does not elaborate on 'backend', 'maxFiles', or 'scope' beyond the schema descriptions. With schema coverage at 50%, this partial compensation is adequate but not comprehensive.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function as a natural-language codebase search with grep-style matching and optional LLM summarization. It distinguishes from siblings by explicitly naming analyze_file and batch_analyze as alternatives for different use cases, and provides concrete question examples ('where is X handled?').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance on when to use this tool ('Use when you DON'T already know which file to look at') and when not, naming alternatives: use analyze_file for a known file and batch_analyze for a structured question across a known set of files. It also clarifies the depth parameter's shallow vs deep behavior.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well: it warns about destructive behavior when write_files is true, explains that files are written to work_directory subdirectories (red/green/refactor), and describes the iteration loop termination condition. It also includes the full return structure, adding useful context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but each sentence earns its place: it covers the workflow, use cases, alternatives, destructive warning, and return structure. It is well-organized and front-loaded with the core purpose, making it dense yet scannable. Not overly verbose for the complexity involved.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, no output schema, no annotations), the description is remarkably complete. It explains the process in enough detail to predict behavior, provides safety warnings, states alternatives, and lists the exact return fields. The only minor gap is not elaborating on cleanup or permissions, but the return structure and schema coverage fill most needs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers 100% of parameters with descriptions, providing a solid baseline of 3. The tool description enriches this by explaining the behavioral impact of write_files (destructive, writes to subdirectories) and mentions max_iterations as the loop limit. This extra context adds value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs a test-first development workflow as a graph of parallel agents, detailing the decomposer, RED/GREEN phases, and quality reviewer. It distinguishes itself from siblings by explicitly naming spawn_subagent and dual_iterate as alternatives, making its unique role obvious.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides explicit when-to-use guidance: 'Use for self-contained features that benefit from test-first discipline and can be parallelized.' It also gives concrete alternatives: 'For a single agent on a single task, use spawn_subagent' and 'For a generate→review→fix loop on one code blob (no test infrastructure), use dual_iterate.' This is exemplary usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states 'Read-only: makes one HTTP call to the chosen backend,' explains the smart-routing vs forced-provider behavior, and enumerates the return fields (including fallback_chain, cache_status, was_truncated). It even discloses edge-case behavior of the 'model_profile' parameter. This gives the agent a thorough behavioral model.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but every sentence carries distinct value: purpose, routing, usage directives, read-only note, and return contract. It is front-loaded with the core meaning and only slightly verbose in the return field list. The detail is justified by the tool's complexity, so a 4 rather than 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description compensates fully by listing all return fields. It covers when to use, routing options, the read-only nature, and even edge cases. No important gaps for a direct-query tool with complex routing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the schema's property descriptions are already unusually rich (model enum semantics, token limits, local model handling). The description adds only minor extra context around 'model:'auto'' and the routing concept. Baseline 3 for high schema coverage is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb ('Send') and the exact resource ('one prompt to one AI backend') plus the outcome ('return the response'). It explicitly contrasts with sibling tools (council, spawn_subagent, generate_file/modify_file), so the tool's scope is clearly distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives an explicit directive: 'Use this for direct LLM queries that don't fit a more specialized tool,' followed by concrete alternatives for common cases (council for multi-backend consensus, spawn_subagent for agentic multi-step, generate_file/modify_file for file edits). This is model usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and excels: it explicitly warns that restore is destructive but reversible via pre_restore snapshot, cleanup permanently deletes files, and create/list are read-only. It also discloses the cleanup policy's both-thresholds condition and return field details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Though longer than typical descriptions, every sentence adds value. It is well-structured with action enumeration, a prominent destructive warning, and a return specification section, earning its length without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity—four actions, nested options, and no output schema—the description fully covers all aspects: action behavior, parameter roles, destructive outcomes, cleanup thresholds, and return shapes for each action. It is self-contained and complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3, but the description adds substantial meaning beyond schema: it explains action-specific parameter application (e.g., file_path ignored for restore/cleanup, backup_id required for restore), clarifies cleanup_options semantics, and defines the return structure per action. This significantly enhances understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool manages timestamped backup files produced by modify_file and write_files_atomic, and enumerates the four specific actions (create, list, restore, cleanup). This distinguishes it from sibling tools that create or modify files, using a specific verb+resource structure.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is clear: it says create for manual snapshot before risky native edit, list to enumerate backups, restore to overwrite, and cleanup per policy. It also mentions dry_run preview. However, it doesn't explicitly name alternative tools for when not to use this one, though siblings like modify_file are referenced as the source of backups.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavioral traits: it warns of destructive behavior when review:false, explains default non-destructive review mode, notes parent directory creation, mentions sibling test file side effect for includeTests, and details return statuses. This is exemplary transparency beyond what any schema could convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is thorough but efficiently structured, front-loading the core purpose and flow, then use cases, warnings, and return values. While slightly long, every sentence carries information. Some redundancy could be trimmed (e.g., both 'Claude either reviews' and 'default review:true is non-destructive' say similar things), but overall it is well organized and not bloated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity, lack of output schema, and rich parameter structure, the description fully covers usage context, side effects, return payload, and review mode behavior. It also ties the output to write_files_atomic for applying reviewed content, ensuring the agent knows exactly what to expect and how to proceed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 67% at top level, with options sub-properties well described. The description adds valuable semantics: clarifies the review flow, explains contextFiles purpose for style anchoring, notes includeTests creates a sibling test file, and defines what each status means. This compensates for the coverage gap and enriches parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Generate a new file from a natural-language spec.' It explicitly scopes to fresh files (boilerplate, scaffolding, test fixtures, single-file utilities) and names sibling tools for contrast, making the tool's unique purpose immediately clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use guidance ('Use for fresh files you can describe by goal') and when-not-to-use with named alternatives: 'For editing an EXISTING file, use modify_file' and 'For writing a known content string to disk with no LLM involved, use write_files_atomic.' This leaves no ambiguity about when to select this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description takes full responsibility. It clearly states 'Read-only — never calls an LLM, never writes to disk,' which is a critical safety disclosure. It also details the cumulative nature of report types and the uninitialized fallback behavior, going far beyond basic expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but efficiently structured: it leads with purpose and safety, then lists report types with their exact return shapes, and ends with the uninitialized case. Every sentence carries necessary information without repetition or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given three optional parameters with rich enums, no output schema, and complex behavior, the description is exceptionally complete. It covers return values for every report type, the cumulative relationship, and an edge case (uninitialized). This is a model description for a complex diagnostic tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although schema coverage is 100%, the description adds significant semantic value: it explains exactly what each report_type returns, how time_range is ignored for some types, and the cumulative relationship of report types. This provides practical understanding that raw schema enums cannot convey.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Inspect SAB's internal telemetry' and enumerates concrete data categories. This clearly distinguishes it from sibling tools like review or ask, which are about coding actions rather than diagnostics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit when-to-use guidance is given: 'Use to diagnose why SAB picked backend X, tune routing rules, or understand cost trade-offs.' It does not mention when not to use or alternatives, but the tool's unique role among siblings makes this implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses critical behavioral traits: Claude never sees file contents (privacy), reads filePath and optionally includeContext files, makes one LLM call, and is read-only. It also explains the verbatim short-circuit behavior and the return shape, exceeding typical transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is lengthy but every sentence adds value. It front-loads the core purpose, then distinguishes alternatives, then explains the short-circuit edge case, then lists the return shape. No filler or redundancy; the structure guides the reader from general to specific details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so the description must explain return values—it does, listing all fields in the return object. It also covers the verbatim short-circuit response variant and the behavior around file reading. Given the tool's complexity (nested options, multiple backends), the description provides comprehensive context for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 67% (below the 80% threshold), so description must compensate. It adds meaning beyond the schema, e.g., clarifying that includeContext files are read and that question drives the analysis. It also explains that backend selection is influenced by analysisType, and describes the output fields like tokens_saved, which are not in the input schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Read ONE file and answer a question about it using a local or cloud LLM.' It also distinguishes from siblings by explicitly naming alternatives (batch_analyze for many files, explore for codebase search) and describing the verbatim short-circuit for line-range questions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit usage criteria: 'Use when you have one specific file and a specific question (security check, bug hunt, architectural concern).' It also gives clear alternatives: 'For the same question across many files (glob patterns), use batch_analyze. For a natural-language search across the codebase with no specific file in mind, use explore.' The line-range short-circuit condition is also explained.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
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 read-only nature: 'Read-only: reads every matched file (capped by maxFiles) and makes one LLM call per file (parallel by default).' It also explains the behavior of aggregateResults and return shapes for all modes, including edge cases like empty pattern match. This is rich, honest behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense but every sentence adds value. It front-loads the core purpose, then usage guidance, alternatives, behavioral notes, and return shapes. There is no fluff or repetition. The structured breakdown of return shapes is efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a complex tool with two output modes, yet the description fully covers purpose, usage, behavioral traits, and return shapes for both modes and the empty-file case. No output schema exists, so the description's detailed return shape specs are essential and complete. The sibling differentiation is also covered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 67% with some param descriptions already present. The description adds meaning beyond the schema by explaining the critical aggregateResults flag's effect on output shape, and mentions the maxFiles cap. It also provides examples for filePatterns in the schema, but the description itself doesn't repeat all options. Slightly above baseline because it compensates for the missing output schema and clarifies key parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Run the SAME question against a glob of files, then aggregate the findings into one cross-file summary.' It distinguishes itself from siblings by explicitly naming alternatives: 'For ONE file, use analyze_file (cheaper). For NL search without a known file set, use explore.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit use cases: 'codebase-wide audits', 'per-feature reviews', 'pre-merge sweeps'. Also gives explicit exclusions and alternatives: 'For ONE file, use analyze_file' and 'For NL search without a known file set, use explore.' This meets the 'when/when-not/alternatives' criterion perfectly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and excels. It discloses destructive behavior with a warning and backup mechanism, explains transaction semantics (all_or_nothing rollback vs best_effort), and clarifies that default review returns diffs without writing. It also details return shapes for both review modes, going well beyond minimal requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but every sentence earns its place. It is logically structured: purpose first, then usage examples, transaction behavior, exclusions, safety warning, and return shapes. No fluff or repetition; the density is justified by the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (batch editing, transaction modes, review modes) and the absence of an output schema, the description is remarkably complete. It covers use cases, alternatives, safety, transaction semantics, and detailed return shapes. There are no significant gaps for an AI agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for `transactionMode` and `review`, explaining rollback behavior and the write-vs-preview distinction. It does not elaborate on `backend`, `parallel`, or `stopOnError` beyond schema defaults, so not every parameter is enriched. Overall, it supplements the schema where it matters most.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb+resource: 'Apply the SAME natural-language instruction independently to each file.' It clearly distinguishes from siblings by explicitly stating it does not find cross-file references and naming `refactor` and `modify_file` as alternatives. Examples make the scope unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance: 'Use for sweeping consistent edits' with concrete examples, then states what the tool does NOT do and names alternatives: 'For symbol renames that must update callers, use `refactor`. For one file with custom instructions, use `modify_file`.' This covers both when-to-use and when-not-to-use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that the tool is read-only ('never writes to disk'), makes parallel HTTP calls, and explains backend selection behavior driven by topic and confidence_needed. With no annotations, the description fully carries the safety and behavior disclosure burden.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with purpose, then usage, safety, and return object. Every sentence adds information, and the return structure is detailed but necessary given the absence of an output schema. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, usage, parameter interactions, safety, latency, and provides a full return object specification. This is especially complete given the tool's complexity and missing output schema. It gives an agent everything needed to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema documents each parameter's type and enum. The description adds value by explaining the relationship between topic and backend routing, and that confidence_needed auto-calculates the number of backends. The mapping of confidence levels to counts is already in the schema, so it doesn't fully go beyond, but the interaction logic is an addition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb+resource: 'Pose one prompt to several AI backends in parallel and return all of their responses for Claude to synthesize.' It clearly distinguishes from siblings by explicitly naming the alternative 'ask' for single backend queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use context ('architectural trade-offs, controversial calls, or anywhere dissent surfaced cheaply'), latency expectations, and an explicit exclusion ('For a single backend query, use ask'). This is textbook usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, and it excels. It discloses that the tool is 'Read-only: returns the generated code to the caller; does NOT write to disk' and explains the internal loop, the fact that Claude sees only the final accepted code, and the meaning of return fields like `history` and `self_review_applied`. It also details the stopping conditions and the role of `quality_threshold`. This is comprehensive behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but every sentence earns its place. It is front-loaded with the core mechanism, then covers usage, alternatives, read-only behavior, and return format. Despite the length, it is densely informative and structured logically. No fluff or repetition of schema details; it adds value throughout.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (internal loop, 4 parameters, no output schema), the description is remarkably complete. It explains the full process, the return object structure (including `success`, `code`, `mode`, `iterations`, `execution_time_ms`, `metadata`, `history`, `final_review`, `self_review_applied`), the read-only nature, and how to persist results via `write_files_atomic`. It also addresses the user-facing benefit (reduced token output). Nothing critical is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema: it explains the effect of `quality_threshold` with concrete ranges ('0.7 (default) = balanced. 0.85+ = strict... 0.5 = lenient') and clarifies that the loop stops at `max_iterations` regardless of threshold. It also links `include_history` to the return value. This enriches parameter understanding without redundancy, so 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Code generation with an internal review loop,' and clearly explains the iterative process. It distinguishes itself from siblings by naming alternatives: 'For multi-agent TDD with parallelism + tests, use `parallel_agents`. For one-shot generation without iteration, use `generate_file`.' This fully clarifies what the tool does and when it is unique.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit usage guidance is provided: 'Use for complex single-file generation where you would otherwise pay the token cost of reviewing iterations in-chat.' It also states when not to use it by pointing to alternatives: `parallel_agents` for multi-agent TDD and `generate_file` for one-shot generation. This gives clear when/when-not/alternatives, exceeding the minimum.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the transparency burden. It discloses the edit mechanism (SEARCH/REPLACE blocks), the size-ratio safety net, the review/write behavior, destructive potential when review:false, backup creation and disabling conditions, dryRun behavior, and detailed return shapes. This is exceptionally transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but information-dense and well-structured: purpose first, then usage guidance, safety warnings, and return shapes. Every sentence provides value, though the returns section is quite verbose. Given the lack of an output schema, the detail is justified, but it could be slightly condensed without losing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is highly complete given the tool's complexity, the absence of annotations, and the absence of an output schema. It covers the mechanism, safety constraints, option behaviors, and all three return shapes (review, dryRun, auto-write). This is a model example of a self-contained tool description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds substantial meaning beyond the schema by explaining the behavioral impact of parameters: review controls approval vs. direct write, backup defaults to true but can be disabled, dryRun produces diff without writing. It also explains how options like backend and contextFiles relate to the editing process. This goes well beyond the schema's field descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Edit an existing file by describing the change in natural language.' It identifies the specific resource (file) and action (edit), and distinguishes from siblings by naming alternative tools for different scenarios (native Edit, write_files_atomic, batch_modify, refactor).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit usage guidance: 'Use for non-trivial edits where the AI does the work.' Then lists clear alternatives with their appropriate contexts, such as 'For a known string→string replacement Claude can do itself, use native Edit' and 'For the same instruction across MANY files, use batch_modify.' This is exemplary when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations to rely on, the description carries the full burden. It warns with a hazard emoji: '⚠️ DESTRUCTIVE when review:false: writes to every file touched by the refactor.' It also explains dryRun's non-writing behavior and details the exact return shapes for review vs. auto-apply modes. This gives the agent a thorough understanding of consequences.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but every sentence earns its place: purpose, use cases, alternatives, safety warning, and return shapes. It is front-loaded with the main purpose and uses scannable formatting (line breaks, emoji) to highlight critical information. No fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a cross-file refactoring tool and the absence of an output schema, the description is remarkably complete. It covers purpose, when to use, how scope works, destructive safety, the dry-run option, and both return shape variants. This is sufficient for an agent to select and invoke the tool correctly in most scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already covers 75% of parameters, and the description adds value by explaining that 'scope bounds how wide the search goes' and listing the enum values inline. It also clarifies the meaning of review/dryRun by describing their effect on the return shape. However, target and instructions are not elaborated beyond the schema, so it doesn't fully compensate for the remaining 25% gap, but it does add meaningful context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a clear statement: 'Cross-file refactoring with automatic reference tracking.' It then details what it does ('Locates where target is defined and where it's used, then applies the instruction consistently across the matched scope') and explicitly contrasts with sibling tools (batch_modify, modify_file). This provides a specific verb+resource+scope and distinguishes from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use it: 'Use for renames, signature changes, API migrations — any edit where consistency between definition and callers matters.' It also provides clear exclusions and alternatives: 'For the same blind edit across files without reference-awareness (cheaper), use batch_modify. For a single-file change, use modify_file.' This is exactly the level of guidance expected.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does this thoroughly: no memory between calls, the agent has no other context than the task, side effects of `write_files:true` (saving code blocks into a work_directory that is auto-created), the default non-destructive behavior, and a detailed list of return fields. It even explicitly flags the destructive condition with a warning emoji. This goes well beyond what a typical description provides.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense paragraph with five sentences, each earning its place: the core action, usage guidance, alternatives, a critical side-effect warning, and a complete return specification. It is front-loaded with the primary purpose and never meanders. No word is wasted.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having 7 parameters, nested objects, and no output schema, the description provides a comprehensive picture: use cases, alternatives, side effects, default behavior, and a full enumeration of return fields. This equips the agent to decide when to invoke the tool and understand its consequences, making it contextually complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% parameter description coverage, so the baseline is 3. The description adds meaningful semantic context that enriches the schema: it explains that `task` should contain file paths and acceptance criteria because the agent has no other context, clarifies the default behavior of `write_files` in terms of resource usage ('adds tokens'), and states the auto-creation/default path for `work_directory`. These additions improve parameter understanding, though not every parameter gets extra treatment, hence a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb ('Spawn') and resource ('one AI agent with a predefined role + system prompt') and clearly states the action ('run it to completion on a single task'). It also distinguishes the tool from siblings by naming `parallel_agents` and `council` as alternatives for different use cases, which removes ambiguity about what this tool is for.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
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 when the work fits a clear role...') and provides concrete examples. It also tells the agent when NOT to use it and what to use instead ('For multi-agent TDD parallelism with quality gates, use `parallel_agents`. For multi-backend consensus on a question, use `council`.'). This is exactly the kind of when/when-not guidance the rubric rewards.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
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 clearly warns 'DESTRUCTIVE', explains backup file creation, details the rollback mechanism (only on later failures, earlier writes restored), and states that backups remain on success. It also discloses throw behavior on mid-batch failure, offering comprehensive transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense but well-structured. It front-loads the core purpose, then provides usage, alternatives, destructive warning, rollback semantics, and return format. Every sentence adds value without redundancy, and the length is justified by the complexity of the atomic batch behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (atomic batch, rollback, backups, append operations) and the absence of an output schema, the description fully covers the return format, edge cases (mid-batch failure, success with remaining backups), and prerequisite safety info. It is complete enough for an agent to use the tool correctly without further clarification.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, providing a baseline of 3. The description adds extra meaning by explaining the behavior of `create_backup` (backups remain, timestamp format) and contextualizing `file_operations` with practical uses like append for logs. It enriches parameter understanding beyond the schema's own descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: writing a batch of files atomically with automatic backup. It specifies the verb 'Write' and the resource 'batch of files', and distinguishes itself from sibling tools like `modify_file` by highlighting the batch and atomicity aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit usage guidance is provided: 'Use this when several file writes must land together' with concrete examples. It also names alternatives: 'For natural-language edits to a single file, use `modify_file` instead' and mentions the append operation for accumulator files, giving clear when-to and when-not-to guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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