impact-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds real behavioral detail beyond that: the response discloses which ranking method was used, and the computation is bounded to 'only the company counts, deal size and percentages you give' — a meaningful determinism claim.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded ('Select a beachhead market'), followed by the conditional behavior. It is dense and delivered as one long semicolon-chained sentence, but each clause carries information about inputs or method selection rather than filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 10-parameter tool with no output schema, the description covers the flow and method selection but never characterizes the return payload (what the ranked segments or TAM/SAM/SOM output looks like). Annotations cover the safety profile, but the result format remains undocumented anywhere.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all ten parameters individually, including the sales_cycle caveat and the range refusal. The description restates the customer_pain/current_customers/average_deal_size interaction that the schema also states, adding little new semantics, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Select a beachhead market', and adds the two concrete outputs (ranked segments and a TAM/SAM/SOM calculation). It is clearly distinguishable from strategy-analysis siblings, though it never names an alternative to route against.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explains the internal method switch ('when you give all three... otherwise keyword presets') and which inputs feed the sizing, which is useful context. However, it gives no guidance on when to pick this tool over impact_full_audit or impact_pinpoint_value, so tool-level selection is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish this is a safe, closed, read-only generation (readOnlyHint=true, openWorldHint=false, destructiveHint=false). The description adds useful shape about the output ('positioning statement and message hierarchy', 'proof and objections'), but says nothing about determinism, downstream use, or what happens when required inputs are thin.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence that leads with the verb and artifact rather than burying it. It is dense but not padded; the only weakness is that cramming sector, business model, proof, and objections into one clause reduces scannability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 9-parameter, 3-required generation tool with no output schema, the description covers the intent and output components but not the return format, how proof/objections are sourced, or edge behavior when optional context (sector, business_model) is omitted. Adequate but with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, including notes on when sector and business_model are inferred from the user's words, so the schema carries the parameter burden. The description adds only the vague phrase 'from your own words' and no field-level semantics of its own, making 3 the correct baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Build) and concrete artifacts (positioning statement, message hierarchy) with the scope modifier 'from your own words' and sector/business-model fitting. It's clearly distinct from a generic generator, but it never names or contrasts the sibling tools (impact_pinpoint_value, impact_map_alternatives), so differentiation must be inferred.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use, when-not-to-use, or alternative-tool routing. 'From your own words' hints that raw, unpolished inputs are expected, but the agent is left to guess how this relates to impact_pinpoint_value or impact_map_alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds useful context that the output includes an input completeness score, implying partial input is tolerated, but says nothing about cost, latency, or how incomplete inputs affect the audit quality.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler; the outcome (score + recommendations) is stated up front. It is efficient, though arguably too terse for a ten-parameter tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 10-parameter tool with no output schema and no annotation coverage of returns, the description is thin: it never says what the audit actually evaluates, how the completeness score is computed, or what the recommendations cover. The only substantive information is one clause about outputs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the enum parameters (your_sector, business_model) are thoroughly documented in the schema itself, so the baseline of 3 applies. The description adds no parameter-level detail beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('positioning audit') and even names its outputs ('input completeness score and recommendations'), so an agent knows what it produces. However, it never differentiates itself from siblings like impact_pinpoint_value, impact_anchor_market, or impact_craft_message, leaving the agent to guess which of the eight impact_* tools to pick.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no prerequisites, and no mention of any alternative. With seven sibling tools covering adjacent positioning tasks, the absence of routing information ('use this for a full audit vs. pinpoint_value for a single dimension') is a real gap.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnly, non-destructive, non-open-world), so the bar is lower. The description adds that output is phased method content that can be localized to a sector, but says nothing about default phase coverage, result size, or what a combined run returns.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence naming the resource, the phasing, and the optional enrichment, with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only guide tool with no output schema and fully documented parameters, the definition is nearly complete. It could say more about what the returned content looks like across phases, but nothing critical is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters including the your_sector precedence rule. The description only echoes sector personalization at a high level, adding no semantics beyond the schema; baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (get) and resource (IMPACT framework) and scopes it as 'phase by phase' with optional sector-specific enrichment. It differentiates from the per-phase siblings only implicitly through 'phase by phase', without naming them, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'optionally with your sector's buyer roles, measures and proof points' implies when to add the sector argument, but there is no explicit when-to-use guidance or routing against the seven single-phase sibling tools. Usage is inferable but not stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description's useful addition is the word 'hypotheses,' signalling speculative output that should be validated rather than treated as fact; beyond that it adds no return-format, latency, or rate-limit context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single tight sentence with the action and output front-loaded and no filler. It is efficient, though the trailing 'using the buying roles of your sector' is the only clause doing extra work.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only hypothesis generator with full schema coverage and no output schema, the definition is minimally adequate. It does not tell the agent what the generated hypotheses look like or how to consume them, which would matter for a multi-sibling analytical workflow.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all six parameters, including the enum and its neutral-fallback behavior, are fully documented in the schema. The description only loosely gestures at 'product context' and 'buying roles of your sector,' adding no syntax or meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Generate') and a concrete output ('champion, economic buyer and technical influencer hypotheses') drawn from 'your product context' via 'the buying roles of your sector.' This clearly separates it from generic analysis tools, but it never names or contrasts with any sibling like impact_full_audit or impact_pinpoint_value.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no when-to-use guidance, no prerequisites, and no indication of which sibling to prefer for related tasks. The phrase 'from your product context' hints at required input but is not framed as a usage condition.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false, openWorldHint=false), so the bar is low. The description nonetheless discloses the output shape — a card per named competitor, status-quo entries, plus sector questions and objections — which is real behavioral information given no output schema exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence that states the action before the details. It is dense and slightly run-on with its colon-plus-list construction, but every clause carries information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 6-parameter tool with no output schema, the description adequately conveys what is produced, but it omits how required inputs (your_product, category) shape the result and gives no guidance on sequencing relative to the sibling impact_* tools. It is minimum viable rather than complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 echoes the schema's semantics ('the weaknesses and strengths you give', 'the questions and objections of your sector') but adds no syntax, formatting, or edge-case detail beyond what the schema already documents.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action (map alternatives) and enumerates the concrete artifacts produced: one card per named competitor, status-quo options, and sector questions/objections. It is clearly distinguishable from siblings like impact_anchor_market or impact_craft_message by subject matter, though it never explicitly contrasts itself with them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to use this tool versus the other impact_* tools, no prerequisites, and no exclusions. The agent must infer the usage context entirely from the topic words 'alternatives' and 'competitors'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered and the description does not contradict it. The description adds the nature of the output (value statements, matrix, proof points) but says nothing about output format, length, or how 'your own figures' are consumed, leaving gaps beyond what annotations supply.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

It is a single front-loaded sentence with no filler, which is appropriately sized. The phrasing is slightly run-on and awkward ('a value matrix for your own figures and proof points that fit your business model'), costing a point but not impeding comprehension.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and a fairly complex 8-parameter creative-generation task, the description should convey more about the expected output and when to invoke it. It names the deliverables but omits any sense of output shape or workflow position, leaving it only minimally complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, including clear enum semantics for your_sector and business_model, so the schema carries the parameter burden. The description adds only the loose phrase 'proof points that fit your business model,' which loosely maps to business_model but contributes no new syntax or constraints beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Generate') and concrete artifacts ('value proposition statements, a value matrix ... and proof points'), so the agent knows exactly what the tool produces. It is distinguishable by content from siblings like impact_craft_message or impact_anchor_market, but it never names or routes against them, so it stops short of full sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no prerequisites, and no mention of any alternative tool despite seven close siblings in the impact_* family. The agent must infer that this is the 'value proposition' stage from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered and the bar is lower. The description does add that output is channel-adapted copy with model-appropriate calls to action, but it says nothing about output structure, length, or that uncovered channels get reported as not covered (a detail buried in the schema).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One dense sentence with the core verb and the channel set front-loaded and no filler. It is efficient, though for a seven-parameter tool it is arguably under-sized rather than optimally concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a generation tool with no output schema, the description should say at least something about the shape of the result, yet it only describes inputs. Sector-notes inference and business-model inference behaviours live in the schema, not the description, leaving the agent to reconstruct behaviour from structured fields.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all seven parameters, including the two enums, are already documented in the schema. The description only echoes the channel list and the business-model-driven CTA behaviour; it adds no format, defaulting, or interaction semantics beyond what structured fields already carry.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Adapt') plus resource ('positioning statement') and enumerates the target channels and that CTAs are derived from the business model. It is clear what the tool produces, but it never distinguishes itself from similar siblings such as impact_craft_message, so the agent must infer the split.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies a precondition (you already have a positioning statement) but gives no explicit when-to-use guidance, no when-not-to-use, and no pointer to any sibling tool for adjacent needs. The agent has to guess whether this or impact_craft_message is the right call.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.