ai-readme-manager

by Draco-Cheng

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Latest release: v1.10.1
Disambiguation4/5
Each tool targets a distinct phase of the AI_README lifecycle: discover, initialize, read, update, validate, and compress. However, the get_context_for_file description embeds update_ai_readme instructions and workflow, which blurs the boundary between reading and writing context.
Naming Consistency4/5
Tool names generally follow a verb_noun pattern and are predictable. Minor inconsistencies exist: singular vs. plural forms (ai_readme vs. ai_readmes) and get_context_for_file does not mention ai_readme like the others do.
Tool Count5/5
Six tools is well-scoped for an AI_README manager. Each tool covers a necessary function—discovery, initialization, context retrieval, updates, validation, and compression—without redundant or extraneous additions.
Completeness5/5
The tool surface covers the full AI_README lifecycle: discover existing files, initialize missing ones, read context, update conventions, validate quality, and compress content. There are no major dead ends; even maintenance tasks are supported.
Average 4.2/5 across 6 of 6 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 22 commits in the last 12 weeks
- Last stable release on August 20, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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 present, so the description must disclose all behavioral traits. It mentions what is checked (token count, structure, content quality) and that results include suggestions, but it does not state whether the tool modifies files, requires permissions, or has side effects. The read-only nature is implied but not explicit.
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 exceptionally concise: two sentences that efficiently convey the tool's purpose and output without any superfluous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (nested config, no output schema), the description is incomplete. It lacks details about return value structure, validation failure behavior, or how suggestions are presented. The agent would need more context to correctly handle the tool's output.
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 parameters are already well-documented in the schema. The description adds minimal extra meaning beyond stating the overall purpose; it does not elaborate on parameter details. Thus a baseline score 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 clearly states the tool's purpose: validating AI_README.md files in a project, checking token count, structure, and content quality. It uses a specific verb ('validate') and resource ('AI_README.md files'), and distinguishes itself from sibling tools like compress, discover, or update.
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 the tool should be used to check the quality of readme files, but it does not provide explicit guidance on when to use it versus alternatives (e.g., after updates, before compression). No when-not-to-use or exclusions are mentioned.
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 full burden. It discloses the return format (index with paths, scopes, coverage patterns) but does not state whether the operation is read-only, its performance implications, or any side effects. The description establishes basic behavior but lacks depth.
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, completely front-loaded with the action and result. Every word serves a purpose with no redundancy.
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?
Despite no output schema, the description specifies what is returned (index with paths, scopes, coverage patterns), which is sufficient for a scanning tool. With good schema coverage, the overall definition is complete.
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 100%, so both parameters are already documented with descriptions. The tool description adds no additional meaning beyond what the schema provides, resulting in a baseline score of 3.
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 purpose: scanning to discover all AI_README.md files and returning an index with paths, scopes, and coverage patterns. This distinctly separates it from siblings like compress, init, update, or validate.
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 for discovery but provides no explicit guidance on when to use this tool versus alternatives like get_context_for_file or init_ai_readme. Sibling names offer some context, but no when-not or exclusions are stated.
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?
No annotations provided, so description carries full burden. It explains that context may differ per directory, multiple calls are expected, and that without it the agent may suggest wrong tech. However, it does not explicitly state that the tool is read-only or non-mutating, though implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with a strong imperative, but it is overly verbose with repeated 'CALL EVERY TIME' sections and a long list of triggers. It could be trimmed significantly without losing clarity.
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 complexity (4 params, no output schema), the description covers usage context thoroughly, including siblings workflow and conflict detection. However, it lacks any description of the return format, which would be important for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for all 4 parameters. The description adds some value for the 'path' parameter with examples and usage notes, but for others like 'includeRoot' and 'excludePatterns', it adds little beyond the schema. Since coverage is high, baseline 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 clearly states 'Gets AI_README context showing project conventions that MUST be followed.' It identifies the verb (get), resource (AI_README context), and scope (project/file-level). It distinguishes from sibling tools like update_ai_readme and validate_ai_readmes by focusing on retrieval.
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 an exhaustive list of when to call, including specific user intents and triggers. Explicitly states 'CALL EVERY TIME' before code changes. Includes a workflow: get_context → update_ai_readme → get_context → Write/Edit, clearly differentiating from siblings.
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?
No annotations provided, so description carries burden. Discloses scanning, creation of root AI_README, and guidance prompts. Could mention idempotency or behavior if AI_README already exists, but overall transparent.
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?
Description is well-organized with sections, front-loaded with purpose, and every sentence adds value. No fluff.
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?
No output schema, but description and workflow provide a clear picture of the multi-step process. Covers prerequisites, steps, and downstream tools. Lacks details on return format but sufficient for agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (all three parameters described). Description does not add significant detail beyond the schema for targetPath or excludePatterns. Baseline 3 applies.
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 initializes and populates empty AI_README files, with specific verb and resource. It distinguishes from siblings like update_ai_readme by focusing on first-time setup.
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' section lists four scenarios (first-time setup, empty reports, new directories, multiple directories). Workflow indicates sequence after other tools. Does not explicitly exclude cases, but the guidance is strong.
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 burden and it largely delivers: it frames the call as a recording action that should happen before continuing work, and specifies content constraints and the 'last resort' treatment of rewrites. It does not explicitly state that the tool mutates the file on disk or what the return/error behavior is, but the schema's operation details and the tool name make the mutation obvious.
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 it is front-loaded with the core purpose and organized into scannable sections and lettered categories. Every section earns its place; it is verbose only because the tool's trigger conditions are complex and high-stakes.
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?
For a complex decision-recording tool with no annotations and no output schema, the description combined with the detailed input schema is sufficient: it covers triggering conditions, the required preceding get_context step, what to record, what not to record, and how to format content. An agent can determine both when to invoke the tool and how 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?
Schema coverage is 100%, so the baseline is 3; the description adds value by adding a default token budget (<400 tokens), requiring actionable conventions only, and prohibiting explanations/examples, which directly shapes the operations.content parameter. It does not re-explain readmePath/projectRoot, but those are fully documented in 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 opening line, 'CALL THIS to record DECISIONS and CONVENTIONS', states a specific action and resource, and the tool name reinforces it. It is clearly distinguished from siblings like init_ai_readme, compress_ai_readme, and get_context_for_file, which create, shrink, or read the file rather than record decisions.
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 exhaustive when-to-call criteria across conflict resolution, architectural decisions, implementation patterns, and undocumented conventions, plus explicit exclusions ('Do NOT record one-off choices or speculative future patterns') and a rule threshold ('affects MORE THAN ONE FILE or FUTURE CODE'). It also embeds a workflow that orders get_context before update_ai_readme and says to continue after recording.
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 meets it thoroughly. It discloses that transformations are deterministic and pure-text, enumerates exactly what is removed, and explicitly states what is NEVER modified: code blocks, inline code, headings, file paths, URLs, and commands. It also preempts confusion by explaining intentional sentence fragments in the output.
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 organized with clear headers and bullets, front-loading the core purpose and when-to-use conditions before diving into mechanics. Every bullet conveys a distinct, decision-relevant fact, and there is no redundant prose or 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 description is highly complete for a 3-parameter tool: it explains when to call, what it changes, what it preserves, and how to preview safely. The only minor gap is that it does not specify what the tool returns on a successful non-dry-run write, but that is not essential for the agent to select and 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 schema covers all three parameters at 100%, so the baseline is 3. The description adds meaningful usage context beyond the schema by insisting on dryRun:true first, explaining that projectRoot is where config is read from, and indicating that dryRun returns a compression preview. This enriches the agent's understanding of how parameters should be used.
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: 'Compress an AI_README.md file using deterministic filler-language removal'. It clearly distinguishes this tool from siblings like validate_ai_readmes, init_ai_readme, and update_ai_readme by naming the exact transformation and explicitly stating it makes no LLM call.
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?
A dedicated 'WHEN TO CALL' section lists concrete triggers: after validation filler-language warnings, high token counts, after init_ai_readme, or whenever reducing token footprint is desired. It also gives a clear operational instruction to use dryRun:true first, which is actionable guidance an agent can follow without inference.
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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