agentic-sdlc-mcp

Server Quality Checklist

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Latest release: v1.9.0
Disambiguation5/5
Each tool targets a distinct SDLC phase or concern: planning, context, issue creation, implementation prep, quality gates, PR review, security, release, handoff, branch protection, workflow audit, and evidence. No two tools have overlapping purposes, making it easy for an agent to select the right one.
Naming Consistency5/5
All tool names follow a consistent snake_case verb_noun pattern (e.g., plan_from_context, create_issue_set, quality_gate_status). The names are descriptive and predictable, aiding agent reasoning.
Tool Count5/5
With 13 tools, the set is well-scoped for the Agentic SDLC domain. Each tool covers a necessary aspect of the workflow without redundancy or bloat.
Completeness4/5
The tool surface covers the entire SDLC lifecycle from planning to release readiness, including security, branch protection, and workflow audits. Minor gaps exist (e.g., no update/close issue, no add comment), but the provided tools enable core workflows effectively.
Average 4.3/5 across 13 of 13 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 114 commits in the last 12 weeks
- Last stable release on July 28, 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.
Add a glama.json file to provide metadata about your server.
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If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, open world, idempotent. Description adds valuable context: repository text is untrusted, caller assertions unverified, and high-confidence prompt injection is omitted from Markdown. 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?
Three sentences, front-loaded with purpose, each sentence adds specific value. No wasted 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 annotations and output schema exist, the description sufficiently covers the tool's behavior. Could elaborate on versioning or packet contents, but the output schema likely covers that.
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 good descriptions. The description restates the subject scope but does not add new parameter-level meaning beyond what the schema already provides.
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 it generates a versioned, read-only evidence packet for one Issue, pull request, or release ref. Distinguishes from sibling tools like plan_from_context or quality_gate_status.
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 explicit guidance on when to use this tool vs alternatives. Only describes what it does, but fails to mention context or exclusions despite having many sibling tools.
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 indicate readOnlyHint=true and idempotentHint=true. The description adds valuable context beyond annotations: required token scope, specific return fields (reviews, status checks, force-push settings), and conclusion categories. This transparency helps the agent understand the tool's behavior and requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, structured with paragraphs and bullet points, and front-loaded with the core purpose. Every sentence adds value with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's function, fallback behavior, token requirements, return value contents, and conclusion categories. With an output schema present and annotations covering safety, the description is complete for an agent to correctly invoke the 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?
Input schema descriptions already fully document all three parameters (100% coverage). The tool description merely restates the parameter names without adding new meaning. Baseline score of 3 is appropriate given the schema does the heavy lifting.
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 reads classic branch protection and repository rulesets for a branch, defaulting to the repository's default branch. The verb 'Read' and specific resources ('classic branch protection AND repository rulesets') make the purpose precise and distinguishable from siblings.
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 is provided on when to use this tool compared to siblings. The description lacks explicit directives on when to choose this tool over alternatives, such as other tools in the sibling list.
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 indicate read-only, idempotent, non-destructive behavior. The description adds value by detailing what the tool returns (blocking issues, rollback template, etc.) and required token scopes, complementing the annotations without contradiction.
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 well-structured with a topic sentence, token scopes, parameter list, and return summary. Every sentence is necessary, no fluff, and information is front-loaded for quick scanning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool complexity (6 params, nested object, output schema exists), the description covers the essentials: what it does, what it returns, and parameter behavior. Could be slightly more explicit about the output schema structure, but overall adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description goes beyond the schema by explaining fallbacks (owner/repo), deprecation (baseRef), and defaults (headRef default branch), adding practical context for parameter usage.
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 it's a pre-release assessment and lists specific checks (CI status, open bugs, CHANGELOG, etc.), but does not explicitly differentiate from sibling tools like quality_gate_status or branch_protection_status, which could be confused.
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 provides token scopes and parameter details, but lacks explicit guidance on when to use this tool versus alternatives. No comparative context is given, requiring the agent to infer usage.
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 indicate readOnlyHint=true, idempotentHint=true, etc. The description adds detail on summary contents and return format, confirming safe, non-destructive behavior without 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 concise (5 lines), front-loaded with purpose, then lists summary components, arguments, and return type. Every sentence is informative with no waste.
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 an output schema exists, the description covers return format ('Markdown PR summary + structured metadata'). Annotations provide safety insight. Could benefit from potential limitations but is largely 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% with clear descriptions. The description mentions and contextualizes parameters ('owner, repo: Repository coordinates', 'pullNumber: The PR to summarise') but adds minimal new meaning 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 explicitly states 'Generate a structured PR summary from the pull request diff and metadata' and lists included components (change overview, affected files, etc.), clearly distinguishing it from siblings like 'review_pr_against_standard'.
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 creating summaries but does not explicitly state when to use or not use this tool over alternatives like 'review_pr_against_standard' or 'quality_gate_status'.
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, destructiveHint=false, and idempotentHint=true. The description adds value by detailing required token scopes per alert type and summarizing the return structure (alert summary, severity, fix order), providing behavioral context beyond the 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?
The description is concise and well-structured, with separate sections for purpose, token scopes, args, and returns. Every sentence is informative and earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is complete given the existence of an output schema (not shown but indicated). It covers token scopes, parameter defaults, and return components. No major gaps are evident.
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 each parameter. The description lists args and defaults but does not add new meaning beyond what the schema provides. 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 reads GitHub security alerts and produces a triage report, specifying alert types (code scanning, Dependabot, secret scanning) and output components. It is distinct from sibling tools like repo_context or create_issue_set, which serve 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 security triage by listing required token scopes and output, but does not explicitly state when to use this tool versus alternatives like sdlc_evidence_packet. No when-not or exclusion guidance is provided.
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, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds useful behavioral details: returns Markdown summary plus structured content, degrades gracefully for missing files. Does not contradict 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: one-sentence purpose, usage guidance, parameter list, return behavior. While somewhat long given 14 parameters, the format is clear and front-loaded. No wasted sentences, but could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (14 parameters, output schema exists, annotations present), the description is complete. It covers purpose, usage, parameter semantics (via schema), behavior (graceful degradation), and return value. No missing pieces.
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 summarizes each parameter in the 'Args' section but adds little beyond what the schema already provides (defaults, behavior). Some parameters get extra context (e.g., includeWorkflows mentions alternative tool), but overall limited added 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?
Description begins with a specific verb and resource: 'Read baseline context for a GitHub repository'. It clearly enumerates what the context includes (metadata, README, package.json, etc.), and distinguishes from sibling tools like plan_from_context, which focus on planning rather than reading.
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 when to use: 'at the start of any SDLC workflow to understand the codebase before planning or creating work items'. Does not explicitly list when not to use or name alternative tools for specific subtasks, 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?
The annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by explaining the ownership check behavior and the return value structure (sorted findings by severity, etc.). There is 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured. It starts with the main purpose, uses bullet points for standards, explains the ownership check in a paragraph, and then lists args and return value. Every sentence adds 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 the 6 parameters, 100% schema coverage, annotations, and an output schema, the description provides sufficient context. It explains the standards, ownership check, parameter defaults, and return value. No major gaps are present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage, so parameters are well-documented. The description adds extra context: default values for standard and checkOwnership, explanation of the ownership check, and that workType is optional and inferred. This provides more meaning 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 that the tool reviews a pull request against Agentic SDLC standards. It enumerates the three standards (basic, strict, security-focused) and mentions the ownership check. This distinguishes it from sibling tools like quality_gate_status or create_pr_summary, 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 does not explicitly state when to use this tool versus alternatives. It implies usage by listing what the tool does (review against SDLC standards), but it lacks explicit guidance on when not to use it or which sibling tools might be more appropriate for related tasks.
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 declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds critical context: scanning scope, token requirements, fallback behavior for `ref`, and the exact flags checked. No contradictions; description complements annotations well.
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 front-loads purpose and is well-structured with sections for args and flags. It is longer than strictly necessary due to detail, but every sentence adds value. Minor reduction in verbosity could improve it.
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 output schema exists, the description covers expected behavior, parameters, flags, and return value format (per-file findings with severity and conclusion). No gaps detected.
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%; each parameter has a description. The description restates `ref` fallback and owner/repo defaults but doesn't add new semantics 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?
Description clearly states the tool scans `.github/workflows/*.yml` for permission declarations and flags least-privilege gaps, which is a specific verb-resource combination. It distinguishes itself from sibling tools like `security_triage` and `review_pr_against_standard` by focusing solely on workflow permissions.
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?
Description specifies required token scope (`repo` or `public_repo` with read access) and identifies three specific flag conditions. While it doesn't explicitly contrast with sibling tools, the context makes its unique role clear. It could be improved by stating when not to use it.
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 readOnly, openWorld, idempotent, non-destructive. The description adds resource cost details (e.g., up to 61 requests for includeRecentPRs) and clarifies that issue/comment text is untrusted, going well beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is lengthy but every sentence provides value, with a clear opening, detailed parameter list, and explicit return sections. Minor redundancy like 'bounded' repetition could be tightened.
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?
With 8 parameters, 1 required, and an output schema present, the description covers all parameter behaviors, constraints, return structure, and risk considerations without omissions.
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?
Schema coverage is 100% but the description adds critical context: includeRecentPRs depends on includeRelatedFiles, includeDependencies is capped, and workType/riskLevel can be overridden by policy. This adds significant meaning 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 'Generate a risk-aware implementation brief for a GitHub issue' – a specific verb-resource combination that distinguishes it from siblings like plan_from_context or repo_context.
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 is used for generating a brief before implementation, but does not explicitly state when to use or when to prefer sibling tools like plan_from_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?
Annotations indicate readOnlyHint, idempotentHint, and non-destructive, which are consistent with the description's claim of evaluation. The description adds value by detailing the return structure (evidence packet, blockers, warnings, next actions, conservative conclusion), going beyond annotation metadata.
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 for purpose and parameter explanation, one sentence for return. No fluff, front-loaded with the core action. Every sentence contributes essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, conditional behavior, output schema), the description covers the main use cases and return structure. It lacks error conditions or edge cases, but annotations and output schema fill some gaps. Overall, it is fairly complete for an evaluation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds significant meaning: e.g., pullNumber is preferred and evaluates more aspects (CI, reviews, policy, labels, mergeability), while ref evaluates CI only. It also explains blockingLabels default and customization. This is a valuable addition beyond the schema's terse descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool evaluates merge-gate evidence for PRs or CI evidence for git refs. It distinguishes between pullNumber (full evaluation) and ref (CI only), and the verb 'evaluate' combined with 'merge-gate evidence' is specific and resource-oriented. This distinct purpose sets it apart from sibling tools like branch_protection_status or release_readiness_check.
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 to use pullNumber vs ref, and explains that blockingLabels can be customized. It lacks explicit 'when not to use' or alternatives, but the guidance is sufficient for typical scenarios. The sibling list implies alternatives, but no direct comparisons are made.
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 the tool as read-only, idempotent, and non-destructive. The description adds significant behavioral context: it is template-based with no LLM call, reads repo labels to avoid inventing non-existent ones, and returns confidence/needsClarification for inferred workType. This exceeds what annotations provide.
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 with clear sections (purpose, args, return). It is fairly concise but includes necessary details. Minor redundancy exists (e.g., workType enum list repeated), but overall it is efficient and front-loaded with key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool complexity (6 parameters, reusable plan generation) and the presence of an output schema, the description is thorough. It explains the phase-by-phase output, confidence/needsClarification, and links to create_issue_set. No gaps remain for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds extra meaning by explaining fallback behavior for owner/repo, the inference logic for workType, and the need to check confidence/needsClarification. This improves usability beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a structured Agentic SDLC plan from a goal and repo context. It specifies the verb 'generate' and the resource 'plan', and differentiates from sibling tools by emphasizing it produces a plan tailored to workType, unlike repo_context or create_issue_set.
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 the tool (e.g., for planning with a goal and repo context) and explains workType inference behavior. However, it does not explicitly state when not to use it or contrast with alternatives like prepare_work_item, which could clarify boundaries.
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, idempotentHint, openWorldHint, and non-destructive. The description adds beyond: explains that missing 'currentStatus' uses system evidence, and caller-authored fields are 'kept unverified'. 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 brief (intro, usage, param list) with no wasted sentences. Every part contributes to understanding the tool's use and parameters.
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 11 optional parameters, high schema coverage, annotations, and mention of output, the description is complete. It states what returns (handoff prompt, repo context, remaining tasks) which suffices.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema itself documents all parameters. However, the description adds value by explaining fallback behavior for 'currentStatus' and that optional fields are unverified, which goes beyond schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the specific verb 'Generate' and resource 'handoff packet' with clear purpose for another AI agent to continue SDLC work. It distinguishes itself from siblings like 'sdlc_evidence_packet' by focusing on handoff context.
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 states when to use: wrapping up a session, before handoff to a specialised agent, or when context is near limit. It does not mention alternative tools or when not to use, but the context is clear enough for correct selection.
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 indicate readOnlyHint=false, destructiveHint=false, idempotentHint=false, openWorldHint=true. The description adds critical behavioral details: dryRun defaults to true for safety, the dry-run output includes per-issue preview, warnings for missing labels/bodies/titles exceeding limit, and the exact repo coordinates. It also describes return values for both dry-run and live creation. 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 well-structured: a one-line purpose, a safety note, a detailed explanation of dry-run output, and an 'Args' list. Every sentence adds value. No unnecessary words. Suitable length for an AI agent to quickly parse.
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 parameter count (5, 1 required), schema coverage (100%), and presence of output schema, the description fully covers the tool's behavior. It explains purpose, safety, parameter details, and return values. An agent can correctly select and invoke this tool with the provided information.
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?
Schema coverage is 100%, but the description adds value beyond the schema: it explains the safety implication of dryRun default, notes that issues can directly accept plan_from_context's issueDrafts, and describes the return format. The 'Args' section provides clear, concise parameter descriptions that complement 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?
Description clearly states 'Split an SDLC plan into GitHub issues' – a specific verb and resource. The title 'Create GitHub Issue Set' reinforces this. Distinguishes from siblings like plan_from_context (which creates the plan) and prepare_work_item (which prepares but doesn't create issues).
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 guidance on dryRun defaulting to true and the requirement to pass dryRun:false for actual creation. The description explains the dry-run output as a human confirmation page. However, it does not directly compare with sibling tools, though the context is clear enough for an agent to infer when to use this tool (after plan_from_context).
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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