impact-mcp

Server Quality Checklist

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Latest release: v2.0.0
Disambiguation5/5
Each tool targets a distinct step in the IMPACT framework, from high-level methodology to specific competitive analysis, market selection, and messaging. There is no overlap or ambiguity between the tool purposes.
Naming Consistency4/5
All tools share the 'impact_' prefix, and most follow a clear verb_noun pattern (e.g., identify_champions, map_alternatives). The exception is 'full_audit', which uses an adjective_noun structure, creating a minor inconsistency.
Tool Count5/5
With 8 tools, the server is well-scoped for covering the IMPACT methodology without being overwhelming or too sparse. Each tool represents a logical phase of the process.
Completeness5/5
The toolkit offers end-to-end coverage: starting with the framework overview, then progressing through champion identification, competitive mapping, value proposition, market selection, messaging, execution, and concluding with a full audit. This leaves no obvious gaps for the intended workflow.
Average 3/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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. It only states what is built, with no mention of side effects, outputs, or operational constraints, which is a significant gap for a tool with 7 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that conveys the core purpose efficiently. It is concise, though it could benefit from a bit more detail to aid understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having 7 parameters and no output schema or annotations, the description is minimal. It does not explain what 'variations' means, the expected output format, or how the tool fits into a broader workflow, leaving the agent under-informed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage for all parameters, so the description does not need to add parameter details. It adds nothing beyond the schema, hence the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('Build') and resource ('positioning statement and message hierarchy'), which distinguishes it from sibling tools focused on different aspects like framework or champions. However, 'with variations' is a bit vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives. There is no mention of prerequisites, contexts, or when not to use it, leaving the agent to infer usage from the tool name alone.
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, the description carries full burden, but it only states the high-level outcome (scoring, recommendations) without disclosing any behavioral traits such as processing behavior, output format, or side effects. This is minimal disclosure for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence of seven words that conveys the core purpose and outputs without waste. Every word is essential and there is no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 8 parameters and no output schema, yet the description provides only a vague promise of 'scoring and recommendations'. It fails to explain what the audit evaluates, how scoring is generated, or the structure of recommendations, leaving significant gaps for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the parameters are already well-documented. The description itself adds no param-specific details, but the schema does the heavy lifting, earning the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a 'Complete positioning audit' with outputs of 'scoring and recommendations', giving a specific verb and resource. It distinguishes from sibling tools by using 'complete' versus their more focused names, though it does not explicitly contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives or how it relates to the sibling tools, leaving the agent to infer that 'complete' means comprehensive without clear direction.
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?
No annotations are provided, so the description carries the full burden. It only states the function but doesn't disclose what the tool returns, whether it has side effects (e.g., read-only analysis), or any limitations. The agent has no indication of what 'analyze' and 'find' result in.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, front-loaded with a clear verb, no wasted words. It is appropriately sized for the content it delivers.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is minimal for a strategic analysis tool with no output schema. It doesn't explain the expected output, how the optional parameters affect the analysis, or what 'whitespace' concretely means in practice. Missing context that would help an agent use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline of 3 applies. The description adds no extra meaning beyond the parameter descriptions already present, which sufficiently document each field.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses active verbs 'Analyze' and 'find' and names the resource ('competitive landscape', 'positioning whitespace'). This clearly distinguishes it from siblings focused on frameworks, champions, messaging, etc., though 'positioning whitespace' is somewhat jargonistic.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. The description doesn't mention situations where siblings should be preferred, nor any prerequisites or workflow context.
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, the description carries the full burden of behavioral disclosure. It only states the output type ('value proposition with quantification and proof points') but doesn't reveal whether the tool asks for more information, what process it follows, or what the final deliverable contains. This is a minimal disclosure that doesn't cover mutation, side effects, or output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It efficiently conveys the core purpose. However, given the tool's complexity (6 params, no annotations), the brevity edges toward under-specification, but the structure itself is clean.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This tool has 6 parameters, no output schema, and no annotations, so the description must carry significant explanatory weight. It doesn't describe the return value, workflow context, or any behavioral nuances. The description is too minimal to enable confident correct invocation, especially among many similar sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters already have meaningful descriptions. The tool description adds a vague hint that quantification and proof points are derived from inputs, but it doesn't explicitly map to specific parameters, offering marginal value over the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Generate value proposition') and adds a distinguishing qualifier ('with quantification and proof points'), which differentiates it from sibling tools like impact_craft_message. However, it doesn't explicitly name alternatives, so it slightly misses the highest bar.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus the sibling tools (e.g., impact_craft_message, impact_identify_champions). The description lacks any mention of prerequisites, sequencing, or exclusions, leaving the agent without context for tool selection.
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?
No annotations are provided, so the description must carry the behavioral burden. It only states the action 'Adapt positioning' with no information about side effects, output format, prerequisites, or what 'translate execution' means operationally. This is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single 7-word sentence with no fluff, effectively communicating the core action. It is front-loaded and concise, though it sacrifices depth for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 5 parameters and no output schema or annotations, yet the description gives no indication of return values, processing behavior, or how channels are applied. It is insufficient for an agent to know what to expect from invoking this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are already well-documented in the schema. The description merely echoes 'channels' and 'positioning' without adding new syntax or semantics beyond the schema, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Adapt' with resource 'positioning' and context 'for specific channels and touchpoints', making it clear this tool tailors positioning to different contexts. It distinguishes from siblings by focusing on channel/touchpoint execution, though it could be more explicit about its distinct output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool vs the seven sibling tools. The description implies usage for channel-specific adaptation but offers no exclusions or alternative recommendations.
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?
No annotations are provided, so the description carries the full burden. It does not disclose behavioral traits such as whether the operation is read-only, what output format to expect, or any limitations. The description only restates the purpose and adds minimal context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that is front-loaded with the key information. It contains no unnecessary words or repetition, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one optional parameter and no output schema, so the description is reasonably complete for its purpose. However, it lacks any mention of how this tool relates to the sibling phase-specific tools, which would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% coverage with a clear description and enum for the focus_phase parameter. The description adds little beyond the schema, only loosely connecting 'phase-by-phase guidance' to the parameter. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as retrieving the complete IMPACT framework methodology with phase-by-phase guidance, using a specific verb and resource. It implies differentiation from sibling phase-specific tools by emphasizing 'complete' methodology, though it does not explicitly name them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool versus the sibling phase-specific tools. The description does not mention alternatives, prerequisites, or scenarios where this tool is preferred, leaving the agent to infer usage from the name and schema.
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, the description carries the full burden of behavioral disclosure. It only offers a vague quality claim ('no blanks, actionable insights') but does not disclose output format, side effects, permissions, or any limitations. This is insufficient for a tool that generates insights.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that is efficient and free of redundant detail. The phrases 'no blanks' and 'actionable insights' are slightly vague but still serve as meaningful qualifiers. It earns its place, though slightly stronger specifics would improve clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, no annotations, and a 5-parameter tool, the description is incomplete. It does not explain what 'champion hypotheses' means, what the output looks like, how to interpret the insights, or any prerequisites. The tool is part of a suite, but standalone it leaves significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema descriptions cover 100% of parameters, so the baseline is 3. The description adds only a general context remark ('from company/product context') that maps to product_description and problem_solved, but it does not enrich the meaning of individual parameters beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Generate') and resource ('champion hypotheses') and identifies the input context ('company/product context'). It is distinct from sibling tools that focus on other activities like value, market, or message.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when company/product context is available, but it does not explicitly state when to prefer this tool over alternatives or when not to use it. No exclusions or alternative tool references are provided.
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 the full burden of disclosing behavior. It mentions the use of scoring and TAM/SAM/SOM framework, giving some insight into methodology, but it does not state whether the tool returns a recommendation, how inputs are processed, or any side effects. This is a significant gap for an analytical tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that captures the essential action and methodology with no wasted words. Every token contributes to understanding the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having a rich schema with 5 parameters, the description lacks any mention of output format, likely return values, or how the scoring/TAM/SAM/SOM framework is applied. With no output schema and no annotations, the tool remains under-specified for an agent to fully anticipate execution results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does 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 not add any parameter-specific meaning beyond what the schema already provides, but it does not need to since all parameters are documented. It neither enhances nor detracts from the schema's clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Select') with a clear resource ('beachhead market') and adds methodology ('scoring and TAM/SAM/SOM framework'). This clearly distinguishes it from sibling tools like 'impact_identify_champions' or 'impact_map_alternatives', which target different aspects of market strategy.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when one needs to choose a beachhead market, but it provides no explicit guidance on when to use this tool versus alternatives. There are no exclusions or references to sibling tools, so the context is clear but lacks explicit direction.
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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