standup-mr
Server Quality Checklist
Latest release: v0.4.0
- Disambiguation5/5
There is only one tool, so there is no possibility of an agent confusing it with another. The tool's purpose is clearly stated and self-contained.
Naming Consistency5/5The single tool name follows the standard verb_noun convention with a descriptive action and object. With only one tool, there are no conflicting patterns to evaluate.
Tool Count4/5The server is narrowly scoped to retrieving standup data, and one comprehensive tool covers that purpose well. It is slightly thin compared to typical multi-tool servers, but the count feels reasonable rather than trivial.
Completeness5/5The tool covers the complete standup workflow: previous day activity, open MR/PR states, pending reviews, and failed pipeline errors across both GitLab and GitHub. No obvious gaps exist for the stated domain.
Average 4/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 80 commits in the last 12 weeks
- Last stable release on
- 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.
Tools from this server were used 2 times in the last 30 days.
This repository includes a glama.json configuration file.
This server has been verified by its author.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
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?
With no annotations provided, the description carries the full behavioral disclosure burden. It clearly states what data is returned, how activity is scoped (previous working day), how open items are bucketed, and that credentials come from the environment or logged-in gh/glab sessions rather than arguments. It does not mention failure modes or rate limits, but the key operational behavior is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loads the core purpose and return contents, and closes with an important credential-safety note. No sentence is wasted, and the structure is easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains both what the tool returns and where credentials come from, which is essential given no output schema and no annotations. It does not document exact output formatting or failure behavior, but the return contents are described in enough detail for an agent to know what to expect. The optional parameters are fully covered by the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters well. The description adds useful context about credential sourcing, which relates to host and provider behavior, but it does not add significant parameter-level semantics 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.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Collect') and resource ('merge-request-based standup data from GitLab or GitHub'), and enumerates the exact returned content: previous working day activity, open MRs/PRs bucketed by state, pending reviews, and failed pipeline error lines. This is unambiguous and fully distinguishes the tool's purpose even without sibling tools to compare against.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for collecting standup-related data from GitLab or GitHub, but it does not explicitly state when to use it versus alternatives or when not to use it. There are no sibling tools and no direct usage conditions, so the usage context is only implied by the tool's name and return-value description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
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