log-search-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only a single tool, there is no possible ambiguity. The tool's purpose is clearly stated and there are no overlapping tools to confuse an agent.
Naming Consistency3/5With only one tool, the naming pattern is technically consistent by default, but there is insufficient surface area to establish a clear pattern. The verb_noun convention (search_logs) is sensible and readable.
Tool Count2/5A single search tool feels extremely thin for a 'log-search MCP server'. The stated domain of searching remote logs typically requires supporting operations like listing log files, fetching specific log content, or filtering by time ranges, which are absent.
Completeness2/5The tool surface is severely underdeveloped for the stated purpose. While search is the core operation, there are no complementary tools to browse log files, retrieve specific entries, or inspect log sources, leaving agents with a dead-end workflow.
Average 2.9/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. The description doesn't state whether this is a read-only operation (assumed given 'search'), whether multiple servers can be queried concurrently, wait times, output format, or whether results are truncated. For a tool accessing remote servers, transparency about non-mutating behavior would be valuable context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence with zero wasted words. It's appropriately front-loaded and conveys the core purpose directly. It could arguably use slightly more detail about return behavior, but the current size is appropriate for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a moderately complex tool with 4 parameters but no output schema and no annotations. The description covers core purpose but omits behavioral details like output format, pagination limits, timeouts, or whether 'all' servers causes sequential or parallel execution. For a tool that may be invoked across multiple servers, more completeness around execution semantics would improve it.
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 fully documents all 4 parameters. Per baseline rule with high coverage, a 3 is appropriate. The description does not add any parameter semantics beyond what the schema provides, which is acceptable given the comprehensive schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches application logs using grep patterns, with a specific verb (search), resource (application logs on remote servers), and mechanism (grep patterns). It doesn't differentiate from siblings, but no siblings exist, so the purpose is unambiguous and complete for a log-searching tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool, what type of logs it covers beyond 'application', or what constraints exist (e.g., permissions, server access requirements). With no siblings, the when-to-use distinction is less critical, but there's no stated context for when this would be appropriate or what alternatives exist.
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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