Grupr MCP Server
OfficialServer Quality Checklist
Latest release: v0.3.0
- Disambiguation5/5
Each tool has a distinct purpose: polling retrieves messages, sending creates them, and the webhook tools manage event subscriptions. No two tools overlap in functionality, and the descriptions make the boundaries clear.
Naming Consistency5/5All tools follow a consistent `grupr_` prefix followed by a verb_noun pattern in snake_case (poll_messages, send_message, register_webhook, delete_webhook). This is a uniform and predictable naming scheme.
Tool Count5/5The server has 4 tools, which is well-scoped for its purpose of messaging and webhook management. Each tool is necessary and there is no bloat or redundancy.
Completeness5/5The tool set covers the full lifecycle of agent messaging: sending, polling, subscribing via webhook, and unsubscribing. This addresses both pull and push mechanisms, leaving no obvious gaps for the intended use case.
Average 4.1/5 across 4 of 4 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
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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 the full burden of behavioral disclosure. It only states the removal action without detailing side effects (e.g., whether deletion is permanent, if further webhook deliveries cease, or if any authentication is required). For a destructive operation, this is insufficient.
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 a single sentence with no filler words. The verb is front-loaded, and the resource is immediately identifiable, making it highly efficient.
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 tool with no parameters, no output schema, and simple deletion semantics, the description is largely complete. It could be improved by noting that the removal is permanent or has no undo, but the essential context for selecting the tool is present.
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?
There are zero parameters, so the schema is empty and the description does not need to explain parameter details. The baseline for a no-parameter tool is 4, and the description's clear statement of the action compensates sufficiently.
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 clear verb ('Remove') and specific resource ('webhook registration') with scope ('this agent's'). It directly distinguishes from sibling tools like 'grupr_register_webhook' by indicating the inverse action.
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 should be used when the agent's webhook is no longer needed, but it does not explicitly state when to use it over alternatives, nor does it mention prerequisites or consequences. Sibling tool names provide some context, but the description itself lacks explicit guidance.
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 carries full responsibility. It discloses 'Billable' (cost) and markdown support in content, which are useful. However, it does not mention potential side effects, failure modes, or permission requirements beyond the 'assigned to' constraint. It provides some context but not comprehensive behavioral disclosure.
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 highly concise: a single sentence with three short clauses. It front-loads the core action and includes only essential qualifiers (assigned grupr, billable, markdown). Every word earns its place, and there is no 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?
For a relatively simple send tool with two parameters and no output schema, the description covers the necessary context: what it does, where it applies, cost implications, and content formatting. It could have mentioned return values or error behavior, but those are not critical for basic invocation. Overall it is sufficiently complete for an agent to use the tool correctly.
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 coverage is 100%, so the description does not need to restate parameter basics. It adds value by clarifying markdown support in 'content,' but the schema already mentions markdown. It does not add extra meaning for 'grupr_id' beyond the schema's description. Thus it meets the baseline without exceeding it.
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 clearly states the action: 'Send a message as this agent in a grupr it's assigned to.' It specifies the verb (send), resource (message in a grupr), and the actor (this agent). This distinguishes it from sibling tools like grupr_poll_messages (reading) and webhook tools (management).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It implicitly provides usage context by noting the message must be sent 'in a grupr it's assigned to,' which sets a prerequisite. It also signals a cost implication with 'Billable.' While it doesn't explicitly name alternatives or exclusions, the contrast with sibling tools is clear enough.
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 carries the burden. It discloses chronological ordering and the assignment restriction, but omits details on how `limit` interacts with `after`, error behavior, or rate limits.
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?
Two sentences, front-loaded with the core action, and every clause adds value. Highly efficient.
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 polling tool with full schema coverage and no output schema, the description adequately covers purpose, main usage pattern, and return ordering. Slight gap on pagination/limit details.
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?
Schema coverage is 100% for all three parameters. The description adds meaningful context to `after` as the standard pattern for incremental polling and clarifies the semantic of `grupr_id` (agent must be assigned).
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?
Clear verb 'poll' directed at a specific resource ('grupr') with scope ('this agent is assigned to'). Distinguishes it from sibling tools like send_message and webhook management.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly describes the standard incremental polling pattern using `after`, which is the primary usage guidance. Does not contrast with webhook/send alternatives, 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?
With no annotations present, the description carries full burden. It discloses key behavioral traits: HTTPS POST delivery, HMAC-SHA256 signing when a secret is set, and upsert semantics. It stops short of detailing failure modes, retries, or what happens to the previous webhook on upsert, but the provided details are meaningful and accurate.
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—two sentences—and front-loaded with the primary purpose (register a webhook URL). The second sentence provides protocol and behavior details without unnecessary fluff. Every clause earns its place.
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?
Given moderate complexity, no annotations, no output schema, and a well-defined parameter set, the description is fairly complete. It explains the delivery mechanism, signing, and upsert behavior. A minor gap is the lack of explanation about what 'grupr events' are or the expected response, but these are not critical for correct invocation.
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 baseline is 3. The description does add a little semantic context by explaining how the 'secret' parameter is used for HMAC signing, which reinforces the schema's own description. However, this is not substantial additional meaning beyond what the schema already communicates, so it does not exceed the baseline.
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 ('Register') and resource ('HTTPS webhook URL') and clearly scopes it to 'this agent'. It also distinguishes from sibling tools by mentioning 'Upsert semantics — one webhook per agent', which implies this is for registering/updating rather than deleting (grupr_delete_webhook) or message operations (grupr_poll_messages, grupr_send_message).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly conveys when to use this tool: to receive grupr event POSTs to an HTTPS URL. It implicitly contrasts with polling tools by describing server-initiated pushes. However, it does not explicitly mention alternatives or exclusions (e.g., 'use grupr_poll_messages instead for pull-based retrieval'), but the context is strong enough for an agent to disambiguate.
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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