ClickSend MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have clearly distinct purposes: one handles text-to-speech calls and the other handles SMS messaging. There is no overlap in functionality, and an agent can easily differentiate between them based on their names and descriptions.
Naming Consistency5/5Both tools follow a consistent verb_noun pattern (make_tts_call and send_sms), using clear action verbs ('make' and 'send') followed by specific nouns. The naming is predictable and readable throughout the set.
Tool Count2/5With only two tools, the server feels thin for a communications platform like ClickSend, which might be expected to support more operations such as checking SMS status, managing contacts, or handling voice calls. The scope appears limited, potentially causing gaps in agent workflows.
Completeness2/5For a ClickSend server, there are significant gaps in coverage: it lacks tools for checking delivery status of SMS or TTS calls, managing contacts, viewing message history, or handling other communication types like email or fax. This incomplete surface will likely lead to agent failures in broader communication tasks.
Average 2.9/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No issues in the last 6 months
- No commit activity data available
- 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Make...calls' implies an action that likely incurs costs and has external effects, the description doesn't mention authentication requirements, rate limits, cost implications, or what happens after the call is made. This leaves significant behavioral gaps.
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 extremely concise at just 5 words, front-loading the essential purpose without any wasted words. Every element earns its place, making it highly efficient for agent comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool that makes external API calls (likely with cost implications) with no annotations and no output schema, the description is insufficient. It doesn't explain what happens after the call, what success/failure looks like, or any system constraints. The context signals indicate this is a non-trivial operation that needs more complete documentation.
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?
The description provides no parameter information beyond what's already in the schema. However, with 100% schema description coverage, all parameters are well-documented in the structured fields, establishing a baseline score of 3. The description doesn't add any additional context about parameter usage or relationships.
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 action ('Make Text-to-Speech calls') and the resource/service ('via ClickSend'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from the sibling tool 'send_sms', which would be needed for a perfect score.
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?
The description provides no guidance on when to use this tool versus alternatives like 'send_sms'. There's no mention of use cases, prerequisites, or contextual factors that would help an agent choose between TTS and SMS options.
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 disclosure. While 'Send SMS messages' implies a write/mutation operation, it doesn't disclose important behavioral traits like authentication requirements, rate limits, cost implications, delivery confirmation, or error handling. The description is minimal and lacks operational context.
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 extremely concise at just 4 words, front-loading the essential information with zero wasted words. Every word earns its place in communicating the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address what happens after sending (success/failure responses), doesn't mention the sibling tool relationship, and provides minimal operational context for a tool that presumably has costs, authentication needs, and delivery considerations.
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%, with both parameters ('to' and 'message') well-documented in the schema itself. The description adds no additional parameter information beyond what's already in the structured schema, so it meets the baseline for high schema coverage without adding extra value.
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 action ('Send SMS messages') and the target resource ('via ClickSend'), providing a specific verb+resource combination. However, it doesn't differentiate from the sibling tool 'make_tts_call' which appears to be a different communication method (text-to-speech call).
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?
The description provides no guidance on when to use this tool versus alternatives. There's no mention of the sibling tool 'make_tts_call' or any contextual factors that would help an agent choose between SMS and TTS communication methods.
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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