Technocore MCP Server
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool addresses a distinct operation: reading room messages, posting messages, and verifying proofs. There is no overlap or boundary ambiguity between them.
Naming Consistency5/5All three tools follow the same verb_noun snake_case pattern: read_room, post_message, verify_proof. The naming is clear, predictable, and consistent.
Tool Count5/5Three tools is well within the ideal range and each serves a specific, non-redundant purpose. The server is tightly scoped without unnecessary bloat.
Completeness4/5Core room messaging (read/post) and proof verification are covered, and the 'since' cursor enables pagination. However, there is no room-listing tool, so agents must know valid room names in advance; this is a minor discoverability gap rather than a blocking omission.
Average 3.9/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 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
This repository is licensed under MIT License.
This repository includes a README.md file.
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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?
With no annotations provided, the description carries the full behavioral disclosure burden. It does not state whether the operation is read-only, what happens on success or failure, whether authentication is required, or whether any state is changed. The verb 'Verify' implies a non-mutating operation but this is never explicit.
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 compact and every sentence earns its place. The core purpose is front-loaded and the parameter explanation follows clearly without excessive wording.
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?
For a single-parameter tool with an output schema, the basic invocation requirements are covered. However, the lack of usage guidance and behavioral disclosure makes it incomplete for an agent deciding whether and how to call it confidently in a broader workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only a title and type for proof_json, but the description adds meaning by specifying that it is the content of contribution-proof.json as a JSON string. This closes the gap left by the 0% schema description coverage.
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 ('Verify') and a specific resource ('a Technocore contribution proof'). It is immediately distinguishable from the sibling tools read_room and post_message, which serve completely 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 Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. The sibling tools are unrelated, so there is no explicit when-to-use or when-not-to-use context to help an agent decide.
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?
With no annotations, the description carries the full burden of behavioral disclosure. It adds useful context about message signing and environment-variable authentication, which goes beyond a bare action statement. However, it does not disclose side effects, persistence, response behavior, or failure modes, so the disclosure is incomplete for a mutation operation.
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 compact and front-loaded with the core purpose, followed immediately by prerequisites and parameter details. Every section earns its place without redundancy or filler.
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?
For a simple two-parameter tool, the description is largely complete: it gives prerequisites, parameter semantics, and a clear action. An output schema exists, and there is no nested-object complexity, so the missing return-value details are not a serious gap. Minor missing language about when to prefer this tool over siblings keeps it from a perfect score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description fully compensates by explaining both parameters: room is given with concrete examples and text is given with a maximum length constraint. This adds substantial meaning beyond the bare schema type/title fields.
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 uses a specific verb ('Post') and a specific resource ('signed message to a Technocore room'), making the tool's function immediately clear. It also distinguishes itself from the sibling tools read_room and verify_proof because 'post' is a write action while those names indicate read/verify operations.
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 provides clear prerequisites by stating that TECHNOCORE_IDENTITY and TECHNOCORE_PASSPHRASE env vars are required, which is useful operational guidance. However, it does not explicitly explain when to choose this tool over read_room or verify_proof, leaving the decision largely implied by the tool name.
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 are provided, so the description must carry the behavioral burden. It communicates a read-only operation through the verb 'read' and explains cursor-based reading via the 'since' parameter, but it does not disclose details like ordering, pagination, or side effects beyond the implied non-mutating nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-structured: a single clear purpose sentence followed by a concise Args block. Every line adds value without redundancy.
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?
An output schema exists, so return-value explanation is unnecessary. All parameters are documented, and the purpose is clear. It loses a point only for not providing explicit guidance relative to the sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description fully compensates. It explains 'room' with concrete examples, 'limit' with a range (1-200), and 'since' as an optional sequence cursor, providing meaning well beyond the bare schema.
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 opens with 'Read messages from a Technocore room,' which names a specific verb and resource. It is clearly differentiated from siblings like post_message, which writes, and verify_proof, which deals with proofs.
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 purpose 'Read messages from a Technocore room' implies when to use the tool, but it does not explicitly state when not to use it or mention alternative tools. The context is adequate but relies on inference.
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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- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
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