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

Annotations already provide idempotentHint=true and non-destructive. The description adds valuable context: updates are partial (patch-style), and changes do not affect previously scored leads until re-scored. This goes beyond annotations to explain the temporal scope of mutations.

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 compact paragraph of four sentences. It front-loads the primary verb ('View or update'), covers both usage modes, and lists parameter groups without redundancy. Could potentially be split into bullet points for better scanability, but it is sufficiently 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 read/write configuration tool with no output schema, the description should describe the return value format. It mentions 'fetch the current config' but does not specify the structure of the response (e.g., that it mirrors the input schema fields). Additionally, no mention of error handling, authentication, or rate limits. Given the complexity (10 parameters, nested custom_rules), the output format is a meaningful gap.

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?

Input schema has 100% description coverage, so baseline is 3. The description adds default values (e.g., 'Default 0.25' for job_title_weight), explains constraints ('each 0–1, should sum to ~1 but not enforced'), and clarifies behavior like 'replaces the existing rule list when provided' for custom_rules. This adds significant context 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 clearly states the dual purpose: 'View or update the global lead scoring configuration.' It specifies the exact verb-resource pair and distinguishes from sibling tools like `lead_score` by noting that the config applies to future calls only.

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?

Explicitly instructs when to fetch ('call with no fields') and when to update ('pass any subset of fields'). It also clarifies scope ('Changes apply to future lead_score calls only') and sets expectations about previously scored leads. However, it does not explicitly list sibling tools to avoid or conditions under which to avoid using this tool.

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?

Discloses that duplicates are skipped (not failed) so partial batches succeed, and specifies the return structure with ingested and skipped arrays. This adds significant detail beyond annotations, which only indicate idempotency and non-destructiveness.

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?

Three sentences, all essential: action and limits, behavior on duplicates, and usage guidance. Front-loaded with core purpose.

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?

Given the single parameter and no output schema, the description covers all important aspects: batch size, duplicate handling, return format, and comparison to sibling tool. Nothing 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 coverage is 100% with detailed property descriptions. The description adds the response shape and references the sibling tool's schema, which is helpful since no output schema is provided. Baseline is 3, but the added output semantics justify a 4.

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 'Add 1 to 100 leads in a single call,' specifying the action and resource. It distinguishes from sibling tool lead_ingest by mentioning bulk imports and directs users to prefer this over repeated calls.

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?

Explicitly advises 'Prefer this over repeated lead_ingest calls for bulk imports (CSV/webhook drops),' giving clear context on when to use this tool and when to use the alternative.

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 provide limited behavioral detail (readOnlyHint=false, destructiveHint=false). The description adds valuable context: each call appends a fresh batch with new UUIDs, returns counts by status and sample IDs. No contradictions.

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?

Concise paragraph front-loading the core action. Every sentence adds value: counts, archetypes, enrichment state, downstream tools, use case, safety, return info. No waste.

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?

Despite no output schema, the description fully explains return values (counts by status plus sample_lead_ids) and mentions use with other tools. Complete for a demo seeding 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?

No parameters exist in the input schema, so the description cannot add meaning beyond schema. With 100% coverage, baseline is 4. No additional parameter info 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 purpose: 'Populate the pipeline with a realistic demo dataset: 14 leads across 5 archetypes'. It uses specific verbs and resources, distinguishing it from sibling tools like lead_ingest and lead_batch_ingest.

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?

Explicitly states when to use: 'Use this to evaluate LeadPipe via MCP Inspector without Hunter, HubSpot, or Pipedrive API keys'. Also notes it is safe to call multiple times. Could improve by suggesting alternatives for real data, but current guidance is clear.

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 indicate idempotent, non-destructive, mutable. Description adds that it updates the lead in place and returns the enriched record, aligning with annotations. Could specify behavior if domain not found, but overall sufficient.

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?

Three sentences efficiently cover purpose, behavior, and sequencing advice. No redundant information, every sentence 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 single-param tool with no output schema, the description covers input, behavior, and recommendation. Minor gap: no mention of error cases (e.g., missing domain). Still comprehensive.

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 describes lead_id as UUID from lead_ingest/lead_search. Description adds context that enrichment uses email domain, implying lead must have one. This adds value beyond schema, so above baseline of 3.

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 enriches a lead with company data using the email domain, listing specific fields. It distinguishes from siblings by advising to run before lead_score for accuracy.

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?

Provides context that enrichment uses built-in knowledge base (no external API) and recommends ordering before lead_score. Could explicitly mention when not to use or alternative tools, but current guidance is clear.

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 mark idempotentHint true and destructiveHint false. The description adds key behavioral details: dry-run when API keys missing, return payload shape (target, count, summary, errors?). No contradictions.

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?

Three concise sentences front-loaded with the main action, followed by essential details. No redundant or vague statements. Every sentence earns its place.

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?

Covers parameters, behavior with missing env vars, filtering options, and return structure. Lacks details on error format or summary structure, but the overall description is adequate for a tool with 3 params and no nested objects.

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 covers 100% of parameters. Description adds value beyond schema by explaining env var requirements for CRM targets, dry-run behavior, and filtering logic (lead_ids vs min_score). This compensates for any ambiguity 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 states 'Push leads to an external destination' and enumerates specific targets (hubspot, pipedrive, google_sheets, csv, json). This distinguishes it from siblings which focus on scoring, ingestion, enrichment, search, etc. No ambiguity.

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?

Provides clear context: when to export, required env vars for CRM targets, and fallback dry-run behavior. However, it does not explicitly state when NOT to use this tool or mention alternatives among siblings, though the purpose differentiation is implicit.

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?

Description explains duplicate error on re-ingestion, which contradicts idempotentHint=true annotation. Annotation contradiction reduces score to 1.

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?

Concise paragraph covering purpose, params, return structure, and error handling. No fluff, front-loaded with key info.

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?

Despite no output schema, description fully details return object (UUID, status, timestamps). Mentions error condition and related tools (lead_search, lead_score). Complete for 11-param 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?

Input schema has 100% coverage with detailed descriptions. Description adds minimal value by summarizing required/optional but does not exceed schema detail. Baseline 3 + marginal gain = 4.

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?

Clear verb-resource pair 'Add a single lead' with explicit required/optional params and return object. Distinguishes from sibling batch ingest by specifying single lead.

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?

States required field (email), explicitly advises using lead_search for upsert behavior, and mentions lead_score to populate score. Provides clear usage context.

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?

The description discloses that the tool uses only locally-available signals (no external API calls), explains mutation behavior with auto_disqualify, and specifies return values (counts, reasons, credit savings). Annotations already indicate readOnlyHint=false and idempotentHint=true, and the description complements these without contradiction.

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 concise 6-sentence paragraph with no redundancy. It progresses logically: purpose, behavioral notes, usage example, return info. Every sentence 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?

The description covers key aspects: purpose, behavior, usage with siblings, and returns. However, it lacks details on the exact return structure and estimation method for credit savings. Given the complexity of nested criteria, these minor gaps prevent a perfect score.

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?

With 100% schema coverage, baseline is 3. The description adds value by explaining the AND logic for criteria and the use of required_tech_stack with Detecto. It provides context beyond schema descriptions, justifying a 4.

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 explicitly states it filters leads against an Ideal Customer Profile before enrichment, providing a specific verb+resource+scope. It distinguishes from sibling tools like lead_enrich and lead_score by emphasizing pre-enrichment filtering and saving credits.

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 clear when-to-use guidance (before enrichment), explains the auto_disqualify behavior, and suggests chaining with Detecto's detect_platform. It also clarifies default behavior when lead_ids is omitted, effectively covering usage context.

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?

Description confirms mutation (lead status update) and idempotency (weighted average of dimensions), adding details beyond annotations (thresholds, breakdown storage). No contradiction with annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true).

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?

Three sentences concisely cover dimensions, weighting, status update, return value, and prerequisite. No redundant words; information is front-loaded.

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?

Given tool complexity (6 dimensions, weighted scoring, status mutation, prerequisite), description covers all essential aspects. No output schema, but return value is described as updated lead with breakdown.

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?

Input schema has full coverage (100%) with detailed description of lead_id (UUID format). Description adds no param-specific info beyond schema, so 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 clearly states the tool computes a 6-dimensional qualification score, updates lead status, and stores breakdown, with specific verb 'compute' and resource 'lead'. It distinguishes from siblings like lead_enrich and lead_qualify by detailing the multi-dimensional scoring and status update.

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 explicitly advises to 'Run lead_enrich first for the most accurate industry/size signals', providing a clear precondition. While it doesn't explicitly state when not to use, the context and singleton parameter make usage straightforward.

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 and idempotentHint=true, indicating a safe, read-only operation. The description adds that it returns {total, leads[]} and mentions API behavior but doesn't contradict annotations.

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 (4 sentences), front-loaded with purpose, then lists filters, pagination, return format, and use cases. No wasted words.

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?

Given 8 optional parameters and no output schema, the description covers all filters, pagination, return shape, and primary use cases. It lacks explanation of status field semantics but the schema's enum descriptions compensate.

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?

Input schema describes all 8 parameters with 100% coverage. The description adds value by explaining AND-combined filters and providing usage examples like 'min_score=40, max_score=60 for borderline leads', which improves understanding 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 clearly states 'Search and filter the lead pipeline' and lists specific use cases like exports and dashboards, distinguishing it from sibling tools such as lead_ingest, lead_qualify, and lead_score.

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 suggests when to use the tool ('drive exports, targeted scoring, and dashboards') but does not explicitly state when not to use it or name alternative tools. However, the context of siblings implies differentiation.

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, idempotentHint=true, openWorldHint=false. Description adds value by detailing the return shape and confirming full-dataset aggregation. No contradictions. Could mention performance or data freshness.

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 sentences, no fluff. Lists return fields compactly and front-loads the core purpose. Every sentence earns its place with zero redundancy.

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?

Given no output schema, description fully specifies all return fields and their structure. Complexity is low (no params). Use cases are covered. No missing information for an agent to understand when and how to invoke it.

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?

Input schema has zero parameters (100% coverage). Description reinforces 'Takes no input', confirming behavior. With 0 parameters, baseline is 4; description adds no extra param semantics but correctly describes the lack of parameters.

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?

Description clearly states it provides portfolio-wide pipeline analytics across all leads, lists all returned fields, and distinguishes from sibling tools like lead_score (individual scoring) by emphasizing aggregation. The verb 'Returns' and context 'Ideal for dashboards...' firmly establish purpose.

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?

Explicitly states 'Takes no input — always aggregates the full dataset' and recommends uses: 'dashboards, stand-ups, and conversion-rate tracking'. Does not explicitly list when not to use, but the context and sibling tool names (e.g., lead_search for filtered queries) imply boundaries.

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