PinchMCP

Server Quality Checklist

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Latest release: v0.4.0
Disambiguation5/5
Each tool has a clearly distinct analytical purpose – from ranking keystones to computing critical paths, explaining blockers, suggesting code links/scope, surfacing gaps, grounding features, reconciling notes, listing projects, and rebuilding graphs – with no overlapping or ambiguous boundaries.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case (e.g., rank_keystones, list_projects, build_feature_graph), with no mixing of conventions or vague verbs.
Tool Count5/5
With 10 tools, the set is well-scoped for a dependency analysis server – enough to cover essential analyses without redundancy or bloat.
Completeness5/5
The tool surface covers the full analytical lifecycle: project discovery, graph building, multi-angle dependency analysis, coupling inference, gap detection, feature grounding, and meeting note reconciliation – with no obvious gaps given the server's read-only, suggestion-only design.
Average 4/5 across 10 of 10 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 94 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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}
```
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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 provided, so description must carry transparency. It mentions transitive analysis but does not disclose side effects (read-only assumed), authentication needs, output format, or rate limits. Minimal behavioral disclosure.
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 with no redundancy. First sentence delivers purpose, second adds parameter detail. Efficient and front-loaded.
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?
Despite low complexity, the tool lacks output schema and the description fails to explain return structure. Requires inference about what 'show' means. Incomplete for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50% (only project_id described). Description repeats project_id's accepted formats but adds no new semantic insight for either parameter. ticket_id lacks description in both schema and text.
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 tool shows transitive blockers and unblockers for a ticket. The verb and resource are specific, and it distinguishes from siblings like 'critical_path' which likely focuses on path analysis rather than block relationships.
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?
Implies usage when needing to understand blocker relationships, but no explicit guidance on when not to use or when to prefer alternatives like 'critical_path' or 'surface_gaps'.
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 provided, so description carries full burden. It mentions 'rebuild' implying overwrite, and 'in-memory' suggests ephemerality, but doesn't disclose side effects, cost, or whether it modifies persistent state. Adequate but not comprehensive.
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, each earning their place. The first explains the action, the second details the parameter. No fluff or 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?
For a simple one-parameter tool with no output schema, the description covers the essential action and parameter. However, it could mention that the built graph is used by sibling tools, which would enhance completeness.
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 description repeats the same info (project name, URL slug, or UUID) without adding new meaning. Baseline of 3 is appropriate as no added 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?
The description clearly states it fetches issues and blocking relations and (re)builds an in-memory dependency graph for a Linear project. This distinguishes it from siblings that analyze or rank existing graphs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives like 'rank_keystones' or 'critical_path'. The agent must infer that this tool builds the graph for subsequent analysis, but no explicit direction is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It states the tool is non-destructive ('suggestions only') and does not create tickets, but fails to mention side effects, error handling, performance considerations, or response structure. For a prediction tool, this transparency is inadequate.
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 two sentences with no redundant content. The first sentence concisely captures the core functionality. The second sentence provides parameter clarification and disclaimers. Slightly verbose but efficient overall.
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?
Given the lack of output schema and annotations, the description should provide a clearer picture of the output format. It hints at predictions for code areas and existing tickets, but does not specify the structure or how to interpret results. It is adequate for basic use but leaves gaps that could confuse an AI 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 description coverage is 100%, so the description adds minimal extra value. It rephrases parameter purposes (e.g., 'repo_path is the local checkout') but does not provide new constraints or usage details beyond the schema. 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 action: predicting code areas and overlapping tickets from a free-text feature description. It distinguishes itself from siblings by mentioning it does not create tickets, directing users to the Linear MCP for ticket creation.
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 provides clear context for when not to use it (ticket creation) and names an alternative (Linear MCP). It also specifies acceptable input formats for project_id. However, it does not explicitly state when to use this tool versus other sibling tools like rank_keystones or surface_gaps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral transparency. It explains the core action but omits details like read-only nature, required permissions, or behavior with edge cases (e.g., empty graph). This is insufficient for a tool performing complex analysis.
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 extremely concise: two sentences covering purpose and parameter flexibility. Every word adds value, with no redundancy or filler.
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?
Given the lack of output schema, the description fails to explain the rank format or how to interpret results. It provides the concept but not enough for a developer to fully understand the tool's output.
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%, and the description adds clarity by specifying that project_id accepts a project name, URL slug, or UUID. This goes beyond the schema description, adding 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 specifies a unique action: ranking tickets by leverage via dominator analysis. It distinguishes this tool from siblings like critical_path and explain_blockers, which have different purposes.
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 identifying high-leverage tickets but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives. It relies on the user to infer context from the technique 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 provided, so description carries full burden. It discloses that the tool computes based on ticket estimates and returns duration chain and slack, but does not mention side effects, auth requirements, or rate limits. Adequate but not comprehensive.
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 with no wasted words. First sentence explains functionality and outputs; second sentence provides usage context and parameter guidance. Perfectly front-loaded.
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 low complexity (one parameter, no output schema or nested objects), the description covers purpose, usage, and parameter type. It mentions output (slack, chain) but not format or example. Mostly 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 description coverage is 100% for the single parameter. Description repeats that project_id accepts name, URL slug, or UUID, which matches the schema. No additional semantic enrichment 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 it computes the critical path via CPM over ticket estimates, defines outputs (longest-duration chain, slack), and explicitly distinguishes it from sibling 'rank_keystones' by contrasting the questions each answers.
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?
Provides explicit guidance on when to use this tool vs sibling (answers 'what sets total duration' vs 'max leverage unlock'). Also clarifies that project_id accepts name, URL slug, or UUID, aiding correct input. No explicit when-not-to-use, but the contrast is sufficient.
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 present, so description carries full burden. It discloses the tool's non-authoritative nature ('never asserted') and dependencies (shared files, imports, co-change). No side effects or auth needs are mentioned, but appropriate for a suggestion tool.
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 only; first sentence conveys purpose and scope with examples, second clarifies nature and parameter syntax. No redundancy, 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?
Description covers input parameters but lacks output format or any example result. For a simple tool with no output schema, this is a gap in completeness.
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 description restates parameter meanings almost verbatim, adding no new insight. 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: infer coupling between tickets from code and suggest missing links. It explicitly distinguishes from siblings by noting suggestions are not folded into keystone/critical_path.
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 provides usage context: suggestions only, never asserted or folded. It implies when to use for non-authoritative link suggestions, but lacks explicit comparison to all 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?
With no annotations, description fully carries behavioral disclosure, clearly stating it is analysis-only and non-destructive. Details what gaps are reported. No mention of rate limits or data freshness, but transparency is adequate for a read-only analysis tool.
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 delivering core purpose and key behavioral context. No fluff or redundancy. Front-loaded with the most critical information (what it reports, that it's read-only, parameter flexibility).
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 single required parameter and no output schema, description adequately specifies input (project_id) and output content (cycles, isolated tickets, keystone gaps). Sufficient for an agent to select and invoke this tool correctly among 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 100% with one parameter fully described. Description repeats the same info (name, slug, UUID) without adding new semantics beyond the schema, 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 reports deterministic planning gaps: cycles, isolated tickets, and keystone tickets missing estimates or owners. It distinguishes from siblings like rank_keystones and critical_path by focusing on hygiene gaps and emphasizing analysis-only behavior.
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 'Analysis only — asserts nothing, writes nothing,' clarifying appropriate usage for read-only analysis. Provides parameter flexibility (name, slug, UUID). Lacks explicit when-not-to-use or alternative tool references for deeper context.
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 discloses a key behavioral trait: 'Suggestions only — pinch never writes', indicating read-only nature. It also implies the tool requires preprocessed input. 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 efficient: four sentences covering purpose, behavioral constraints, and parameter details with no redundant words.
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 no output schema, the description implies output are suggestions, but doesn't specify structure. It covers input, behavior, and parameter format adequately for a reconciliation tool.
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%, but the description adds value by explaining repo_path's purpose ('enables code-area grounding') and framing items as 'blockers, ticket refs, feature/bug mentions', which clarifies the schema's discriminated union.
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 reconciles structured items from meeting notes against a Linear dependency graph, specifying the verb (reconcile) and resource (notes vs graph). It distinguishes from siblings like 'explain_blockers' by targeting extracted items.
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 explains when to use ('extraction is the client's job') and when not ('pinch never writes; use the Linear MCP to act'). It also clarifies parameter format (project_id accepts name/slug/UUID). However, it doesn't explicitly compare to all 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 full burden. It discloses the tool is a suggestion only and not authoritative. However, it does not explicitly state it is read-only or describe side effects, though the context implies no destructive actions.
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, no fluff. Purpose and mechanism in the first sentence, usage constraint in the second, and parameter details in the third. 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?
No output schema, and description does not specify the return format beyond mentioning predictions of code areas and coupled tickets. It also omits potential error conditions. For a simple tool with minimal parameters, it covers essentials but could be more 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%, but the description adds value by clarifying that project_id accepts name, slug, or UUID, and repo_path must be an absolute path. This aids the agent in correct parameter formatting.
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: predicting code areas and coupled tickets for tickets without code by matching text against a keyword index. It uses specific verbs and resources, and distinguishes from sibling tools like rank_keystones and critical_path.
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 ('planning aid') and when not to use ('never used in keystone/critical_path'), providing clear context and implicitly contrasting with 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 are provided, so the description carries the burden. It discloses the output contains ids and slugs and explains why they are needed (Linear lookup requires UUID/slug). Lack of mention of pagination or ordering is a minor gap, but for a simple list tool it is mostly adequate.
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 concise, front-loaded sentences with no redundant information. Every word serves a purpose.
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 zero parameters and no output schema, the description fully equips an agent to use the tool correctly: it explains the output format, the use case, and why the tool is needed. No important gaps remain.
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?
There are zero parameters and schema coverage is 100%. The description adds value beyond the schema by detailing the return values (ids and slugs) and their purpose, which is sufficient for the agent.
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 lists the workspace's Linear projects with their ids and slugs, specifying the exact purpose of enabling project_id selection for subsequent operations. It distinguishes itself from sibling tools which are unrelated.
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 implies when to use (when you need a project ID for Linear operations) but does not explicitly state when not to use or provide alternatives. However, sibling tools are sufficiently different that no exclusion is necessary.
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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