Structured website and review evidence for AI-assisted local-business shortlisting.
Compare two to six businesses after the user has gathered website and review scores. Calculates a transparent shortlist score using 60% review trust and 40% website evaluability.
| Name | Required | Description | Default |
|---|---|---|---|
| candidates | Yes | Two to six businesses using comparable evidence. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It discloses the scoring methodology and the valid input size (two to six), which goes beyond the schema. It does not describe the output format or side effects, but a pure comparison tool implies no side effects.
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?
The description is two sentences with no fluff: the first states the scope and prerequisite, the second states the calculation formula. Every clause adds value.
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 simple one-parameter tool with a well-described nested schema, the description covers purpose, prerequisites, and scoring formula. It omits an explicit description of the return type, but the calculation nature and sibling context make the output inferable.
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?
The schema already provides 100% parameter coverage with descriptions for each field. The description adds the critical semantic meaning: the 60/40 weighting between review trust and website evaluability, which is not present in 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 clearly names the action ('Compare two to six businesses') and specifies the exact scoring formula (60% review trust, 40% website evaluability), distinguishing it from siblings like inspect_website or score_website_evaluability.
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 states the prerequisite condition ('after the user has gathered website and review scores'), providing clear timing. It does not explicitly mention alternatives or when not to use the tool, but the workflow context makes the intended usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Turn a ShortlistLens result into a concise fixed-scope implementation brief for a Buyer-Readiness Scorecard or a 72-hour AI Buyer-Readiness Sprint.
| Name | Required | Description | Default |
|---|---|---|---|
| business_name | Yes | The business to scope. | |
| priority_gaps | Yes | Top gaps identified by the diagnostic. | |
| review_trust_score | Yes | Review-trust score from supplied evidence. | |
| website_evaluability_score | Yes | Website score from the diagnostic. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It does not disclose whether the tool is read-only, whether it modifies any state, or any potential side effects or limitations. The lack of any behavioral notes leaves the agent uncertain about safety or resource implications.
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?
The description is a single, focused sentence with no unnecessary words. It efficiently conveys the purpose and key context, making it easy to parse and understand.
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?
The description provides sufficient context for a straightforward generation tool: it names the inputs and the intended output. While it does not elaborate on the format of the brief or any assumptions, the simplicity of the tool means this is likely complete enough. A dedicated output schema would have made it fully 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?
All four parameters are described in the schema, though quite minimally. The description adds no additional meaning beyond what the schema already states, such as how the scores relate to the generated brief or any expected value ranges. With 100% schema coverage, a baseline of 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 clearly states the tool's function: turning a ShortlistLens result into a fixed-scope implementation brief for a Buyer-Readiness Scorecard or 72-hour sprint. It distinguishes itself from sibling tools (e.g., compare_shortlist_candidates, inspect_website) by specifying a unique output deliverable.
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 usage when a ShortlistLens result is available but offers no explicit guidance on when to prefer this tool over alternatives. It does not mention any conditions, prerequisites, or scenarios where other tools would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a public business website page and return structured, accessible facts for a buyer-readiness review. It only examines the supplied public URL and does not scrape review platforms or private systems.
| Name | Required | Description | Default |
|---|---|---|---|
| website_url | Yes | A public HTTP or HTTPS page URL to evaluate. | |
| target_service | No | Optional service the buyer is researching, such as pool renovation or roof replacement. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility for disclosing behavior. It explicitly states what the tool does (fetches and returns facts) and what it does not do (does not scrape review platforms or private systems). This transparency is sufficient for users to understand the tool's scope and limitations.
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?
The description is concise, consisting of two sentences that contain all essential information without redundancy. It is well-structured, presenting the main action and then clarifying constraints and exclusions.
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?
The description is adequate for a simple fetch tool, covering purpose, scope, and parameters. It could be slightly more explicit about the format of the returned 'structured facts,' but given the lack of an output schema and the tool's simplicity, the current description is sufficiently complete for most users.
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?
The schema provides clear descriptions for both parameters: website_url is a public HTTP/HTTPS URL to evaluate, and target_service is an optional service the buyer is researching (e.g., pool renovation). These descriptions align with the tool's purpose, and the schema coverage is 100%, so no additional explanation is needed.
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 clearly states the tool's function: fetching a public business website page and returning structured facts for buyer-readiness review. It also distinguishes itself from sibling tools by explicitly noting it does not scrape review platforms or private systems, making its purpose unambiguous.
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 provides usage context by specifying it is for buyer-readiness review and that it only examines the supplied public URL. This implies when to use this tool (for inspecting websites) versus others (like compare or score), but it does not explicitly list alternative scenarios. However, the boundaries are clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Organize user-supplied public review excerpts into transparent positive, risk, owner-response, and corrective-action signals. The tool does not fetch reviews itself and does not adjudicate any claim.
| Name | Required | Description | Default |
|---|---|---|---|
| excerpts | Yes | One to fifty user-supplied public review excerpts. | |
| review_count | No | Optional public review count supplied by the user. | |
| overall_rating | No | Optional public overall rating supplied by the user. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the burden. It discloses that the tool does not fetch reviews itself and does not adjudicate claims, indicating it is a pure organizer with no external data retrieval or decision-making. This transparency is adequate for a non-destructive, data-processing tool, though it does not mention side effects or state changes (which are likely none).
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?
The description is two sentences with no wasted words. It front-loads the primary action and output, then clarifies limitations in the second sentence. Every phrase contributes to understanding the tool's role and boundaries.
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 tool with 3 parameters, no output schema, and a well-defined scope, the description is complete. It explains the input source, the transformation, and what the tool does not do. Given the tool's simplicity, no additional context is necessary.
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 coverage is 100%, providing baseline 3. The description adds meaning by specifying that inputs are 'user-supplied' and that the output is organized into 'transparent positive, risk, owner-response, and corrective-action signals,' which clarifies the purpose and output categories beyond the raw schema. This adds value without duplicating schema details.
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 clearly states the verb (organize), the resource (user-supplied public review excerpts), and the specific output categories (positive, risk, owner-response, corrective-action signals). It distinguishes itself from siblings by clarifying it does not fetch reviews or adjudicate claims, making its scope unique.
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 usage when review excerpts are already available and categorization is needed. It explicitly notes the tool does not fetch reviews or adjudicate, which helps set expectations for when it should be used. However, it does not name alternative tools or provide explicit when-not scenarios beyond its non-fetch/non-adjudicate statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Inspect a public business website and score how easily a buyer can identify the business, understand a target service, verify proof, and take the next step. Returns an explainable 0–100 rubric and priorities.
| Name | Required | Description | Default |
|---|---|---|---|
| website_url | Yes | A public HTTP or HTTPS page URL to evaluate. | |
| target_service | No | Optional service the buyer is researching. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears the burden. 'Inspect' implies non-destructive read-only behavior, and 'public' constrains applicability, but side effects, permissions, or data handling are not disclosed. No explicit read-only claim, but the nature is inferred.
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?
Two concise sentences with no redundancy. Directly states action, evaluation dimensions, and output. Efficient and well-structured.
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?
Though no output schema exists, the description explicitly explains the return (explainable 0–100 rubric and priorities) and enumerates the evaluation criteria. Sufficient for a straightforward scoring tool.
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 already covers both parameters with descriptions; description adds context by defining the evaluation purpose (buyer ease) and clarifying that target_service refers to the service being evaluated. Adds meaning beyond basic URI/string definitions.
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?
Clearly states it inspects a public business website and scores buyer ease across four criteria (identify, understand, verify, take next step), with an explicit mention of the output (0–100 rubric and priorities). Distinct from sibling tools.
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?
No guidance on when to use this tool versus the siblings (e.g., compare_shortlist_candidates, inspect_website). The description implies it's for evaluating a single site's evaluability, but does not explicitly state conditions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
compare_shortlist_candidatesgenerate_paid_scopeinspect_websiteorganize_review_evidencescore_website_evaluabilityClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Owner-verified local business data for AI agents: profiles, hours, prices, with provenance.
Local business intel for AI agents: audits, lead scoring, tech stack, prospecting.
Verified local businesses, bookable by AI agents: services, prices, availability and appointments.
AI visibility + fact-checks (ChatGPT/Perplexity), review gaps & competitor scans, local SEO.
Add one secure layer between your agents and this server.
Most tools have clearly distinct roles: inspect_website gathers facts, score_website_evaluability produces a rubric score, organize_review_evidence handles review signals, compare_shortlist_candidates merges scores, and generate_paid_scope produces a brief. The only mild ambiguity is between inspect_website and score_website_evaluability, since both involve website inspection, though their outputs differ.
All tool names follow a consistent verb_noun pattern: compare, generate, inspect, organize, and score are clear actions with descriptive objects. The naming is predictable and makes the tool set easy to navigate.
Five tools is well-scoped for the server's niche buyer-readiness purpose. Each tool contributes to a focused workflow without redundancy or unnecessary sprawl.
The workflow covers website inspection, website scoring, review evidence organization, comparison, and downstream brief generation. However, compare_shortlist_candidates expects a review trust score, and no tool actually computes that score from organized review evidence, leaving a notable gap in the core shortlisting flow.
