harness-bridge

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Server Quality Checklist

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Latest release: v0.4.1
Disambiguation4/5
Most tools have clearly distinct purposes: reviewing code, reviewing plans, explaining code, performance planning, and implementing are all distinguishable tasks. The main overlap is between codex_query and codex_explain_code, both of which handle general questions/understanding, though query is broader and explain is specifically for code explanation.
Naming Consistency4/5
All tools follow a consistent codex_verb_noun pattern with clear verbs (query, review, explain, plan, implement) and snake_case throughout. Minor deviation is that codex_plan_perf uses an abbreviated 'perf' rather than 'performance', and codex_query breaks the noun-object pattern slightly compared to others.
Tool Count4/5
Six tools is a reasonable, well-scoped count for a harness that bridges to a specific LLM. Each tool covers a distinct use case and none feel redundant or unnecessary, though it's on the smaller side and could arguably be consolidated.
Completeness4/5
Coverage is solid for the stated purpose of bridging to Codex: query, explain, review code, review plan, performance plan, and implement cover the major interaction modes. Minor gaps include no explicit test-generation tool and no way to summarize or compare results across queries, but agents can work around these via codex_query.
Average 3.4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 15 commits in the last 12 weeks
- Last stable release on August 10, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. The description does not mention serialization behavior for shared threadKey, the model override semantics, working directory defaults, or any side effects/cost/time implications of invoking Codex. The threadKey serialization behavior lives only in the schema, not the description. It does not describe return format or async behavior either.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence that states purpose and use cases without waste. It is appropriately front-loaded with the verb and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters (1 required) and no output schema, the description is moderately complete. However, it lacks behavioral context that would help an agent: whether calls are blocking/long-running, cost implications of invoking Codex models repeatedly, and guidance on when to use threadKey vs one-shot. The no-annotation reality means the description should carry more behavioral weight than it does.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description list of use cases adds minimal parameter meaning beyond the schema's own descriptions. The description doesn't add guidance on prompt construction quality or formatting beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Ask... a question or give it a task') and resource (OpenAI Codex). It names a few use cases (second opinion, exploring unfamiliar code, different model's perspective), though it doesn't explicitly distinguish itself from the codex_* siblings beyond these generic 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions use cases ('second opinion, exploring unfamiliar code, tasks that benefit from a different model's perspective') that imply when to use it, but provides no explicit exclusions or guidance contrasting with sibling tools like codex_review_code, codex_explain_code, or codex_implement. Usage is implied, not clearly delineated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It does state the WARNING that this modifies the codebase and mentions it returns a summary of changes. However, it doesn't disclose execution time, serialization behavior with threadKey, or whether changes are reversible/committed.
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?
Three short sentences with the critical warning front-loaded in the middle. Zero waste, effectively communicates danger and return value. Could arguably be more compact, but the structure is clean and scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is somewhat thin. It warns about codebase modification and mentions the summary return, but a more complete description would note that changes should be reviewed/verified, might persist in working tree, and that this is an expensive/long-running operation relative to siblings.
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 coverage is 75% (3 of 4 params described in schema). The description adds minimal parameter meaning beyond schema, though the model param's 'omission' guidance IS in the schema itself. The description does not add details about workingDirectory semantics or task format beyond what schema provides. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'implement a feature, fix a bug, or make code changes' via Codex. The verb+resource is specific ('Ask Codex to implement'). However, it doesn't explicitly distinguish this from siblings like codex_query or codex_review_code, though the code-modifying nature is implied.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies it's for implementation/code-changing tasks and explicitly warns 'This modifies your codebase,' which signals it's not appropriate for read-only inquiries. However, it doesn't explicitly name alternatives for read-only tasks (like codex_query for Q&A or codex_review_code for review) or provide when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the tool 'identifies gaps, risks, missing edge cases, and suggests improvements', which is a good behavioral disclosure of what outputs look like. However, it doesn't mention potential side effects, latency, whether it modifies anything, or whether it consumes substantial resources. The outputs listed are helpful but partial.
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?
Two sentences, no filler. First sentence states purpose, second sentence enumerates what the review produces. Efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a review tool with 5 params and no output schema, the description is reasonably complete but lacks detail on how the review is delivered (return format, whether it's blocking/async) and doesn't clarify the distinction from codex_review_code beyond implication. Adequate for basic use but an agent might not know how the review output presents.
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 80%, which is high. The description doesn't add parameter-level detail beyond what the schema provides. Since coverage is high (>80%), baseline is 3. The description names no specific parameters but the schema already documents plan, threadKey, constraints, codebasePath adequately, so the description adds no extra value but doesn't need to.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states what the tool does: 'Ask Codex to critique an implementation plan' and lists specific outputs (gaps, risks, missing edge cases, suggestions). Verb+resource are specific. It distinguishes from siblings like codex_review_code (code review) and codex_plan_perf (perf planning), though it doesn't explicitly name them, so it loses a point on sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for use ('critique an implementation plan') but provides no explicit when/when-not guidance or alternative tool references. An agent could infer this is for pre-implementation review, but it doesn't state 'use instead of codex_review_code when reviewing plans rather than code' or similar exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It conveys the semantic intent (explain/understand/document) but doesn't disclose key behavioral traits: it doesn't state whether this is read-only, whether it modifies files, whether it has rate limits, mutates state, or sends code to an external service. It adds context about the purpose but omits operational behavior.
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?
Two sentences, front-loaded with the core purpose, then use cases. No wasted words. A third sentence naming alternatives could have improved the differentiation but the current length is appropriate and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a relatively simple explanation tool with a well-documented schema (80% coverage) and no output schema, the description is moderately adequate. It captures the intent and use cases. However, the absence of any behavioral/operational disclosure (read-only? external service? cost implications?) and minimal sibling differentiation leave gaps that reduce completeness, especially given the large sibling set.
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 80%, meaning most parameters are documented in the schema itself. The description adds value beyond the schema by contextualizing the tool's overall purpose, which helps interpret 'target' and 'context' semantically. The 'depth' enum is already well-described in the schema. The description doesn't add per-parameter detail beyond schema, but with high coverage that's acceptable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb+resource ('Ask Codex to deeply explain code, logic, or architecture') and lists use cases. However, it doesn't explicitly distinguish from sibling tools like codex_query or codex_review_code, though the 'deeply explain' phrasing implies a pedagogical intent that differs from query or review.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives useful when-to-use context ('understanding unfamiliar code, onboarding, or documenting complex systems') but doesn't explicitly contrast with alternatives like codex_review_code (review) or codex_query (search). No when-NOT-to-use guidance is provided, and with several siblings, explicit differentiation would be valuable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. The description discloses that it 'identifies bottlenecks' and produces 'ranked optimizations with expected impact', which gives some behavioral context about output. However, it doesn't disclose whether it modifies code (mutating) or only produces a plan, required permissions, side effects, or how the plan is delivered. For a tool whose verb is 'ask Codex to analyze', the mutating-vs-read nature is ambiguous.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, both substantive, with zero waste. The first sentence states the purpose, the second elaborates on what analysis entails. Front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 6 parameters and no output schema, so the description needs to convey what the user gets back. It mentions 'ranked optimizations with expected impact' which hints at the deliverable. However, for a tool with no annotations and no output schema, it doesn't fully clarify whether the output is a plan document, an in-conversation response, or a written artifact, nor the mutability/side-effect profile. Adequate but with clear 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?
Schema description coverage is 83%, so most parameters (target, context, metrics, threadKey, constraints) are already documented in the schema. The description adds the 'target' purpose implicitly as the optimization focus. With high schema coverage, baseline is 3, and the description adds little beyond what the schema already provides, though 'metrics' enum values align with performance focus.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('analyze performance and create an improvement plan'), identifies the resource (Codex), and specifies scope ('identifies bottlenecks, proposes ranked optimizations with expected impact'). It distinguishes from siblings like codex_query and codex_review_code by focusing specifically on performance analysis and improvement planning, though it doesn't explicitly name sibling alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it (performance analysis/optimization scenarios) but provides no explicit exclusions or comparisons to alternatives such as codex_review_code or codex_review_plan. There's no guidance on when this tool should NOT be used or how it differs from the review/plan siblings, so the usage context is only implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The description states it 'Returns specific, actionable feedback,' which indicates the output nature. However, it doesn't disclose that this delegates to an external LLM service (Codex), potential latency/rate-limit behavior, or what 'review' entails operationally. The phrase 'Ask Codex' does hint at external delegation, providing some transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence (plus a return-value note) that conveys the core purpose without waste. It front-loads the verb and resource, immediately establishing what the tool does. It's appropriately sized-nothing extraneous, though it could arguably mention a sibling alternative for differentiation without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a moderately complex tool with 5 parameters and no output schema, the description provides the essential purpose and input modalities but lacks depth. It doesn't clarify expected return format (actionable feedback presumably, but structure is unknown), doesn't describe threading behavior beyond what the schema's threadKey field implies, and doesn't caution about external-service behavior. The description is adequate but leaves gaps for an agent to make informed decisions about invocation.
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 80%, so most parameters are already documented in the schema itself. The description adds value by clarifying the 'target' parameter's three acceptable forms (git diff range, file paths, code snippet) with a concrete example ('HEAD~3..HEAD'), which goes beyond the schema's description. The 'context' and 'focusAreas' parameters are self-explanatory in the schema, and the description doesn't need to add much there.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb+resource ('Ask Codex to review code') and identifies three input modalities (git diff range, file paths, code snippet). However, it doesn't distinguish from sibling tools like codex_explain_code or codex_query, which also involve Codex code interaction. The purpose is clear on what the tool does but not clearly differentiated from its siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the input modality examples (diff range, file paths, snippet), which hints at when this tool applies. However, there is no explicit when-to-use guidance, no exclusions, and no mention of alternatives like codex_explain_code for explanation-focused queries versus codex_review_code for actionable review feedback. Usage context is implied but not explicitly stated.
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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