Cynical Sally

Server Quality Checklist

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Latest release: v0.7.0
Disambiguation5/5
Each tool targets a specific domain (ideas, code explanation, frontend, marketing, refactoring, PR reviews, general code review, usage) with clear boundaries. Overlaps are minimal and well-differentiated in descriptions.
Naming Consistency5/5
All tools follow the 'sally_{action_or_target}' pattern in lower_snake_case, providing a predictable and consistent naming convention across the set.
Tool Count5/5
Eight tools cover the core functionality of the cynical feedback server without being too few or too many, striking a good balance for the intended scope.
Completeness5/5
The tool surface covers key areas: idea critique, code explanation, frontend review, marketing review, refactoring, PR review, general code review, and usage tracking. No obvious gaps for the domain of cynical analysis.
Average 4.6/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 12 commits in the last 12 weeks
- Last stable release on July 2, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, openWorldHint), the description adds critical behavioral context: data is never stored or used for training, it is read-only ('never modifies files'), and it includes premium usage limits. No contradiction with 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 well-structured: starts with core purpose, then usage guidelines, privacy, and premium info. It is slightly verbose but each sentence adds value. No waste, but could be tighter.
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 tool has 2 parameters (both documented in schema), no output schema, but the description explains the return format ('markdown verdict with risks and a bright side'), along with privacy and limits. Enough for the agent to understand behavior fully.
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% and the schema descriptions are detailed. The tool description adds only a minor confirmation that only the provided description is sent to the backend, not additional semantics. 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 the tool's purpose: to evaluate an idea or architecture for scalability flaws, using a specific verb ('Pitch...Sally names the three ways it falls apart'). It distinguishes itself from sibling tools (e.g., sally_roast, sally_explain) by focusing on pre-build critique and scalability.
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?
The description explicitly says when to use ('Use when the user wants feedback on an idea... before building it') and provides context about premium tiers and limits. However, it does not explicitly list when not to use or directly contrast with sibling tools, which would have earned a 5.
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?
Beyond readOnlyHint, adds no-storage/no-training privacy guarantee, markdown output format, and premium tier info. Does not detail any potential dynamic behavior but adds significant context.
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?
Description is moderately long (5 sentences) but every sentence serves a purpose: purpose, usage, privacy, read-only, output, premium. Front-loaded with core action.
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?
Covers all essential aspects for agent invocation: purpose, when to use, privacy, read-only nature, return format, and usage limits. No output schema but return format is described.
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 baseline is 3. Description adds minimal extra meaning: only mentions that 'content' is the code and 'lang' is optional. Sufficient but not enhanced.
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?
Clearly states the verb 'explain' and resource 'code', with a distinctive tone ('plain English, no hand-holding'). Differentiates from siblings by focusing on code explanation rather than brainstorming or refactoring.
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?
Explicitly describes when to use: 'when the user wants a snippet or file explained, asks what does this do, or inherited code nobody documented.' Provides clear context and implicit alternative avoidance.
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?
Beyond the readOnlyHint and openWorldHint annotations, the description adds key behavioral details: text is sent only to the backend, never stored or used for training. It also states that it never modifies files, aligning with annotations. No contradictions.
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 three sentences, each adding essential information: purpose, usage/output, and behavioral/privacy details. No wasted words and front-loaded with the most important info.
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 simple two-parameter tool with no output schema, the description covers purpose, usage, behavioral transparency (privacy, read-only), and output format (before/after rewrites with reasoning). It also mentions premium tier limits. 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 the schema already documents both parameters. The description adds no significant new meaning beyond the schema, meeting the baseline 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 that Sally reviews marketing copy, branding, and landing-page text, and specifies that it returns before/after rewrites with reasoning. This distinguishes it from siblings like sally_roast or sally_review_pr, which likely have different focuses.
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?
The description explicitly says 'Use when the user wants copy, taglines, or brand messaging critiqued' and notes the read-only nature. It could be more explicit about when not to use, but the context and sibling names provide enough differentiation.
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?
Annotations already indicate readOnlyHint and openWorldHint. Description adds key details: output is markdown, code is never stored or trained on, usage limits (one free use/month). No contradictions.
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 efficient: clear purpose sentence, usage directive, privacy guarantee, read-only note, and premium tier info. Every sentence adds unique value without 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?
Given no output schema, the description adequately explains output as markdown with before/after code. It covers privacy, behavior, and usage limits, making it complete for a simple 2-parameter tool.
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?
Both parameters are fully described in the input schema (100% coverage). The description does not add extra detail about lang or content beyond what the schema provides, so 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 performs refactoring with concrete before/after code and explains why original code is poor. It distinguishes from siblings like sally_roast (roasting) and sally_explain (explaining) by focusing on code improvement.
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?
Explicitly states 'Use when the user wants code improved, cleaned up, simplified, or modernized.' Provides clear context but no explicit alternative suggestions or when-not-to-use flags.
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?
Description states 'Read-only: never modifies files', 'never stored, never used for training', and explains preview mode for verifying what is sent. Perfectly aligns with readOnlyHint:true and openWorldHint:true 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?
Well structured with purpose up front, then input options, mode, privacy, and preview info. Each sentence adds value, though slightly verbose; could be trimmed slightly 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?
Fully covers all aspects of a complex 7-parameter tool without output schema: purpose, input methods, modes, privacy, security (preview), sharing, and return format (markdown). No gaps.
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%, baseline 3. Description adds value by recommending paths over files, explaining mode differences, preview functionality, and share option. Provides practical guidance beyond schema definitions.
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 it provides a 'brutally honest code review' with a 0-10 score, issues, and fixes. It distinguishes from siblings by focusing on roasting and reviewing code, with specific verbs like 'review', 'critique', 'roast'.
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?
Explicitly says to use when user wants code reviewed, critiqued, roasted, or asks what Sally thinks. Differentiates between paths vs files inputs and quick vs full_truth modes. Does not explicitly state when not to use or provide alternatives among siblings.
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?
Description adds context beyond annotations: never stored, never used for training, read-only, returns markdown with categorized issues. No contradictions with annotations (readOnlyHint, openWorldHint).
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?
Four sentences, each adding distinct value: core function, usage scope, privacy/behavior, output format and pricing. No 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?
Given no output schema and multiple frontend types, the description sufficiently covers tool behavior, safety, return format (markdown with categorized issues), and usage limits. Complete for agent understanding.
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 baseline is 3. Description does not add significant new meaning beyond what the schema already provides for the two 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 it roasts frontend/UI code, listing specific types (HTML, CSS, JSX, Vue, Svelte), and distinguishes from the sibling sally_roast for general-purpose code.
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?
Explicitly says when to use this tool (for frontend/UI code) and when not to (use sally_roast instead). Also mentions premium tier usage limits, providing context for agent decision-making.
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?
Annotations already declare readOnlyHint=true. Description adds 'Read-only: never modifies files' and 'never stored, never used for training.' Provides additional behavioral 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise but complete. Front-loaded with core purpose. Each sentence adds value: purpose, usage, privacy, read-only, return format, pricing. No 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?
Moderate complexity with 2 parameters, no output schema. Description explains return format 'markdown review with a verdict and concrete findings.' Sufficient for agent to understand outcome.
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% with descriptions for both parameters. Description adds context: 'Sends only the provided diff to the Cynical Sally backend' and explains diff format (e.g., git diff main) and lang examples. Adds value beyond 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?
Description clearly states it reviews a PR diff like a senior engineer with opinions. Verb and resource are specific, and it distinguishes from siblings by emphasizing catching what automated tools miss.
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?
Explicitly says 'Use when the user wants a pull request, commit, or unified diff reviewed before merging.' Provides usage context. Does not explicitly list when not to use, but the context is clear.
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?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds value by explaining what data is sent ('only the anonymous device ID') and that no code or personal data is transmitted, reinforcing the read-only nature.
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 concise at 4-5 sentences, front-loaded with the main purpose. Every sentence adds essential context without redundancy. Efficient and well-structured.
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 tool with zero parameters and no output schema, the description is fully complete: it explains inputs (none), behavior (read-only, sends anonymous device ID), and output (markdown account summary). No gaps.
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?
No parameters exist, and schema coverage is 100% (empty). The description states 'Takes no parameters,' which is sufficient. Baseline for 0 parameters is 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 clearly states the tool checks the user's Cynical Sally quota and account status, listing specific details like tier, remaining roasts, Full Truth reviews, per-tool premium trials, and email. It distinguishes from siblings like sally_roast, sally_explain, etc., by focusing on usage and account status.
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?
Explicitly states when to use: 'when the user asks how many roasts they have left, what plan they're on, or why a Sally tool just hit a quota wall.' Also clarifies it takes no parameters, providing clear context for use.
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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