Spec-Driven IntelliMatch / iSuite MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Tools are well-separated by domain prefixes (im_ vs is_), and within each domain, most tools have distinct purposes. However, there is some overlap between API and database query tools for the same entity (e.g., im_get_candidate vs im_get_candidate_db), which could cause confusion despite different descriptions.
Naming Consistency5/5
All tools follow a consistent lowercase underscore convention with a verb_noun pattern (e.g., im_get_candidate, is_submit_claim). No mixing of styles or irregular patterns, making naming highly predictable.
Tool Count3/5
With 28 tools spanning two distinct domains (IntelliMatch and iSuite), the count is on the higher side but each domain justifies its tools. The scope is broad, so the number is borderline appropriate but could be seen as excessive for a single server.
Completeness4/5
Each domain covers core operations: IntelliMatch includes searching, matching, and pipeline status; iSuite includes claims, policies, and workflows. Minor gaps exist (e.g., no candidate update or claim modification), but overall the surface is sufficient for common tasks.
Average 3.5/5 across 28 of 28 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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 provided, and description omits behavioral details like side effects, auth requirements, error handling, or idempotency. While 'Generate' implies read-only, this is not explicitly stated.
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?
Single sentence under 20 words, front-loaded with key action and resource. No unnecessary words. Could be considered slightly terse but achieves conciseness.
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?
Description lists report contents (matches, scores, placement rates, job families), offering moderate completeness. No output schema or error condition information, which is a gap given the lack of annotations.
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 covers all three parameters with descriptions for from_date and to_date. Description adds 'date range' context but does not explain the optional 'department' parameter. Schema coverage is 100%, so description adds minimal value.
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?
Description clearly states verb 'Generate' and resource 'IntelliMatch summary report', listing included metrics. Distinguishes from sibling tools like im_get_placement_metrics and im_get_match_score by describing a combined summary report. However, no explicit differentiation in usage context.
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 versus alternatives such as im_get_placement_metrics or im_get_match_score. Does not specify prerequisites or appropriate use cases.
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 full burden. It mentions 'from the SQL Server database' but does not disclose whether the tool is read-only, requires special permissions, has performance implications, or what happens if the year parameter is missing (though required). The description implies a read operation, but lacks explicit behavioral traits.
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 concise sentence of about 20 words, front-loaded with the action and outputs. It is efficient and contains no unnecessary words. Slightly more structure (e.g., listing output types separately) could improve scannability, but it is well-formed as is.
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?
Given no annotations, no output schema, and two simple parameters, the description explains what the tool returns (counts, liability, averages) but lacks behavioral context like error handling, performance, or data freshness. It is minimally adequate for a summary tool but missing details that would help an agent use it confidently.
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 coverage is 100%, so parameters are already described in the schema with standard descriptions. The description does not add extra meaning beyond what the schema provides; it mentions high-level outputs but not how parameters affect results. Baseline score of 3 is appropriate when schema covers parameter documentation.
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 returns a summary of claims with specific metrics (counts by status, total liability, average settlement). It uses a specific verb 'Get' and identifies the resource ('iSuite claims from SQL Server database'). While it does not explicitly differentiate from siblings like is_get_claim or is_search_claims, the summary nature and database source provide implicit distinction.
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 versus alternatives (e.g., is_get_claim for individual claims, is_search_claims for filtered lists). The description implies analytical use but does not specify prerequisites, exclusions, or when not to use it.
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 burden. It mentions that the tool 'returns claims with current status and amounts', but lacks details on pagination, search behavior, or any side effects. Minimal transparency beyond basic output.
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?
Two concise sentences with no wasted words. Front-loaded with the verb 'Search' and clearly indicates the resource 'insurance claims'. Ideal length for quick comprehension.
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?
With 4 parameters (only 25% schema covered), no output schema, and no annotations, the description is under-informative. It omits the claim_type and limit parameters entirely, and does not explain the response format. Incomplete for the tool's complexity.
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 only 25% (query documented in schema). The description adds meaning to the query parameter by listing example inputs (claim number, policy number, claimant name). However, claim_type and limit are not addressed, leaving gaps for the agent.
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 that the tool searches insurance claims by various criteria (claim number, policy number, claimant name, or status). It distinguishes from siblings like is_get_claim (single claim) and is_submit_claim (submission) by focusing on search functionality, though not explicitly.
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 versus alternatives. With siblings like is_get_claim for exact lookups, the description should mention usage context, but it does not.
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 must fully disclose behavioral traits, but it only states the basic action. It does not mention whether the note is appended or overwrites, what happens if the claim does not exist, or any authentication or idempotency details. The description is minimal and leaves the agent uninformed about critical behavior.
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, concise sentence with no wasted words. However, it lacks structure such as bullet points or sections that could improve readability. It is appropriately sized but could benefit from better organization for a tool with multiple parameters.
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, the description should explain what the tool returns (e.g., a confirmation or error handling). It does not cover response format, success indicators, or error scenarios. For a mutation tool with moderate complexity, the description is incomplete and leaves gaps in understanding the tool's full behavior.
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 67% (2 of 3 parameters have descriptions). The description adds no additional meaning beyond the schema; it mentions 'adjuster note or status update' which loosely covers note_type but does not explain the enum values. The description does not enhance understanding of parameters beyond what the schema already provides.
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 'Add' and identifies the resource as 'an adjuster note or status update to an existing iSuite claim.' This clearly distinguishes it from sibling tools like is_submit_claim (which submits a new claim) and is_get_claim (which retrieves). The description directly aligns with the note_type enum values, providing a clear purpose.
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 alternatives. For example, there is no mention that the claim must exist or that this tool is appropriate for adding notes rather than other operations. The description lacks any contextual cues for selecting this tool among many siblings.
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 disclose behavioral traits. It is a read operation but does not mention permissions, rate limits, or data volume. The description 'all open' suggests no upper limit, which might be risky. More transparency like 'returns all matching results, may be large' would improve.
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?
Two sentences, both pertinent. No fluff. However, could be slightly expanded to include output format or limitations without sacrificing conciseness.
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?
Given the lack of output schema and annotations, the description covers the tool's purpose and use case but omits details like output structure, potential limits, and error handling. Fairly complete for a simple read tool but not fully.
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 already covers both parameters with descriptions. The description adds that the query returns headcount and age, which are not in the parameter schema but are output fields. This adds some value but does not explain parameter formats or interactions. With 100% schema coverage, baseline is 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 it queries for all open job requisitions with headcount and age, distinguishing it from similar tools like im_get_job (single job) or im_search_jobs (filtered search). However, it does not explicitly name these alternatives.
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?
It says 'Useful for dashboards and bulk analysis,' indicating it is for aggregated data retrieval. It does not specify when to avoid this tool or mention alternative tools for single-record retrieval. The guideline is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It states the tool returns a score and breakdown, but does not discuss side effects, permissions required, or read-only nature. It provides basic but not comprehensive transparency.
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 extremely concise: two sentences that efficiently convey the core function and output. No unnecessary words, making it easy to parse quickly.
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 description gives the essential function and output information, but lacks details such as whether the tool requires both entities to exist, error handling, or the exact structure of the 'breakdown'. Given no output schema, the description could be more complete.
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 coverage is 100%, so the baseline is 3. The description adds little beyond the schema: it references 'specific candidate and job' but does not elaborate on parameter formats or constraints. The schema descriptions are generic, and the description does not compensate significantly.
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 calculates a match score between a specific candidate and job, and specifies the output range (0-1) and that it includes a breakdown. It is clear and precise, but could better distinguish from sibling batch match tools like im_match_candidates_for_job.
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 usage guidelines are provided. There is no indication of when to use this tool versus alternatives like the batch matching tools or how it differs from them. This lack of guidance could lead to incorrect 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 carries full burden. It only states that results contain requirement summaries but does not disclose behavioral traits such as authentication requirements, rate limits, or that the tool is read-only. Minimal transparency beyond basic functionality.
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?
Two concise sentences with no wasted words. The first sentence states purpose and filters, the second describes the return. Front-loaded and efficient.
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?
Given 5 parameters and no output schema, the description is brief but covers core purpose. However, it lacks details on pagination (limit behavior), result ordering, or how to handle empty results. Also does not help distinguish from sibling tools, leaving context gaps for an 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?
The description reinforces the filter parameters (title, department, location, status) that are already documented in the schema with 60% coverage. It does not add new details for the 'limit' parameter or explain enum values. Adequate but not compensating for missing schema descriptions.
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 searches IntelliMatch job requisitions by specific filters (title, department, location, status) and returns open positions with requirement summaries. However, it does not differentiate from sibling tools like im_get_job or im_get_open_jobs_db, which have overlapping functionality.
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 for searching jobs by multiple criteria but provides no explicit guidance on when to use this tool versus alternatives like im_get_job (single job) or im_get_open_jobs_db (list all open jobs). No when-not or alternative tool names are mentioned.
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, so description must cover behavior. It states 'advance' but doesn't disclose idempotency, effects of repeated calls, or that comments are required for 'reject'/'escalate' decisions (schema indicates required). Missing return/invocation details.
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?
Single sentence efficiently conveys core purpose. Could be slightly longer to cover behavioral details without losing conciseness.
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?
No output schema or annotations; description lacks info on return values, error cases, permissions, and specific decision behaviors. Incomplete for a mutation 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 coverage is 100%; description adds minimal value by summarizing 'Provide a decision and optional comments.' Does not clarify that comments are required for certain decisions.
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 'Advance' and resource 'iSuite workflow', clearly distinguishing from sibling tools like 'is_get_workflow' or 'is_list_my_workflows'.
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 advancing a workflow step but lacks explicit when-to-use, prerequisites, or when not to use. No alternatives mentioned.
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 fully disclose behavior. It does not mention read-only nature, authorization requirements, rate limits, or error handling (e.g., what happens if the policy number is invalid).
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?
Single sentence, direct, and efficient. Every word serves a purpose with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description reasonably explains the return contents. It could be more complete by mentioning possible error states or that it returns a single policy object. Overall, adequate for a simple retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides 100% coverage for the single parameter with a clear description. The description adds value by listing the types of data returned (coverage, premiums, etc.), which helps the agent understand what to expect.
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 gets complete policy details and lists specific elements (coverage, premiums, etc.). It distinguishes the tool from is_get_policy_db by implying a more complete result, but does not explicitly differentiate from siblings like is_get_policy_db.
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 versus alternatives such as is_search_policies or is_get_policy_db. The description does not mention any prerequisites or limitations.
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 provided, so description carries full burden. Does not disclose if operation is read-only, permissions needed, or any side effects. Fails to specify output format (e.g., image URL or base64).
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?
Single sentence of 16 words, directly states action and purpose. No fluff; efficient and front-loaded.
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?
Tool is simple with one param and no output schema. Description explains what it does but omits how result is returned or any limitations. Adequate for basic understanding, but lacks completeness for an agent to use without assumptions.
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 coverage is 100%, so schema already documents the single parameter 'workflow_id'. Description adds 'Workflow to capture' which repeats schema; no additional value like format or constraints.
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?
Clearly states 'Take a screenshot' as verb, identifies resource as 'iSuite workflow', and specifies context 'browser UI' and purpose 'visual review or documentation'. Distinguishes from siblings like is_get_workflow which return data.
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 versus alternatives (e.g., is_get_workflow for data retrieval). No prerequisites or exclusions mentioned.
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?
Lacks disclosure of behavioral traits such as pagination behavior, result ordering, error handling, or what 'key details' includes. Since no annotations are provided, the description carries the full burden but fails to cover these aspects.
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?
Two sentences, front-loaded with the key action and search criteria. Efficiently communicates purpose and output, but misses practical details like default values and result format, which could be added without much verbosity.
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 the tool has 4 parameters including enums and no output schema, the description is incomplete. It does not mention default behaviors (product_type defaults to 'all', status to 'active'), pagination via limit, or what constitutes 'key details' in the response. This could lead to incorrect assumptions.
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?
Adds context beyond the schema by explaining that query can be policy number or policyholder name, and that product_type and status are search criteria. However, the limit parameter is not described, and with only 25% schema description coverage, the description partially compensates but leaves gaps.
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?
Clearly states the tool searches iSuite insurance policies by multiple criteria (policyholder name, policy number, product type, status) and returns matching policies. Differentiates from siblings like is_get_policy (single fetch) and is_search_claims (different resource).
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?
Implies usage for finding policies by search criteria, but does not explicitly state when to use this tool versus alternatives like is_get_policy for a known policy ID. No 'when-not' or exclusion guidance 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 full burden for behavioral disclosure. It mentions the tool runs an AI matching engine (implying compute cost) and returns scores/reasoning, but does not disclose whether it has side effects, requires specific permissions, or has rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, front-loaded with the action and outcome. Every word adds value, with no redundant or missing information relative to its length.
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 description covers the core purpose and output but lacks parameter details, differentiation from siblings, and behavioral context (e.g., whether candidate_id must exist, or the return format beyond 'ranked jobs with scores'). Given no output schema, more detail on the return structure would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 67% description coverage (candidate_id and min_score have descriptions, limit does not). The description adds no additional meaning beyond the schema; for example, it does not explain what candidate_id refers to or the effect of min_score and limit.
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 runs IntelliMatch's AI matching engine to find best-fit job openings for a given candidate, and specifies it returns ranked jobs with match scores and reasoning. This distinguishes it from the sibling `im_match_candidates_for_job` which does the reverse.
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 the tool is for matching jobs to a candidate but does not explicitly state when to use it versus alternatives like `im_match_candidates_for_job` or `im_get_match_score`. No exclusions or prerequisites are mentioned.
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 disclose behavioral traits. It states the tool returns a list with profile summaries, which is minimal. It does not mention whether the operation is read-only, any side effects, rate limits, or pagination behavior. For a search tool, more transparency on output format or limits would be expected.
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 concise with two sentences: the first clearly states the action and search criteria, the second states the return value. No unnecessary words, and key information is front-loaded.
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 and no annotations, the description should provide more details about the returned 'profile summaries' and any pagination or limits. It does not explain the format of the results, and it omits behavioral context like whether the search is fuzzy or exact. The tool has four parameters, but the description only covers the main search criteria, missing details on the 'limit' parameter's purpose.
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 75% (3 of 4 parameters described). The description reiterates the schema's purpose for query, status, and location but does not add new meaning. The 'limit' parameter lacks a schema description and is also not mentioned in the tool description, so the description does not compensate for that gap.
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 it searches candidate profiles by name, skills, location, or status and returns a list with summaries. The verb 'search' and resource 'candidate profiles' are specific, and the tool is distinct from siblings like 'im_get_candidate' (single candidate) or 'im_search_jobs' (jobs).
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 indicates when to use the tool (to search for candidates), but it does not provide explicit when-not-to-use guidance or mention alternative tools. Context from siblings suggests alternatives exist, but the description itself lacks this guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey all behavioral traits. It mentions the return value (claim number and initial status) but omits side effects like creating a new record, triggering workflows, or potential idempotency. The transparency is moderate but not comprehensive.
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, well-structured sentence. It efficiently conveys the purpose and return value without extra words. Slight improvement could be made by structuring the return info separately, but it remains concise.
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?
For a submission tool with 5 parameters and no output schema, the description provides essential purpose and return info but lacks context on post-submission behavior or validation steps. Given sibling tools handle notes and retrieval, more detail on the claim lifecycle 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?
With 80% schema description coverage, the baseline is 3. The description adds no additional parameter-level information beyond what the schema already provides (e.g., no further explanation of claim_type enum values or acceptable description format).
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 action ('Submit a new insurance claim') and the resource ('in iSuite'). It also specifies the return values ('assigned claim number and initial status'). This distinguishes it from sibling tools like is_get_claim (retrieval) and is_search_claims (search).
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 does not provide guidance on when to use this tool versus alternatives. It lacks prerequisites (e.g., policy must exist), instructions on avoiding duplicates, or any explicit context about when to use this tool over other claim-related tools like is_add_claim_note.
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 fully disclose behavioral traits. It implies a read operation but does not explicitly state read-only, nor does it mention authentication, rate limits, or potential side effects. The description is too brief to be informative beyond the basic function.
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, well-structured sentence that front-loads the purpose and includes key details without unnecessary words. It earns its place by efficiently conveying the tool's functionality.
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?
Given the lack of an output schema, the description should provide more detail about the return format. While it lists the metrics (total placements, time-to-fill, department breakdown), it could also include information on data recency, aggregation level, or example response structure to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the parameter semantics are already clear. However, the description adds value by mentioning 'breakdown by department,' which is not reflected in the schema, and by clarifying the output includes specific metrics, aiding the agent in understanding what the tool returns.
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 identifies the tool's purpose: retrieving placement metrics (total placements, time-to-fill, breakdown by department) from the IntelliMatch database for a given period. It uses a specific verb ('Get') and resource ('placement metrics'), distinguishing it from sibling tools like im_get_candidate or im_get_application_logs.
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 does not provide any guidance on when to use this tool versus alternatives, nor does it mention prerequisites, limitations, or exclusions. For example, it does not specify that this tool is for aggregated metrics and not individual records.
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, and the description only mentions querying the database directly without disclosing behavioral traits such as read-only nature, potential performance impact, authorization requirements, or data freshness. This is insufficient for a direct database query 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 concise with two sentences, no redundancy, and front-loads the key action and purpose. Every word adds value.
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 the tool has 4 parameters, no output schema, and no annotations, the description is incomplete. It lacks parameter explanations, behavioral details, and return value information, making it insufficient for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50%, but the description adds no information about parameters. It does not compensate for the missing parameter descriptions, leaving agents to guess the meaning and usage of parameters like status and limit.
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 queries a SQL Server database for policy records, with explicit use cases like bulk analysis and custom reporting, distinguishing it from sibling tools such as is_get_policy and is_search_policies.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly recommends using this tool for bulk analysis, custom reporting, or complex filters, providing clear usage context. It does not explicitly state when not to use it, but the guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It states the tool lists workflows and shows pending actions and due dates, but does not explicitly state it is read-only, mention pagination behavior (despite a limit parameter), or discuss authentication requirements. The name implies a safe read operation, but more clarity would improve transparency.
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 concise with two sentences, no fluff, and front-loads the key action. However, it could benefit from structured formatting (e.g., bullet points) to improve readability, especially given multiple parameters.
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 description covers the core functionality and hints at output (pending actions, due dates), but with no output schema, it should describe the return format. It also does not address edge cases (e.g., empty results, error handling). For a tool with four parameters and many siblings, it is adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is low (25%), yet the description adds little value beyond the schema. It mentions filtering by status and workflow type but does not elaborate on the enums or explain the assignee and limit parameters. The assignee parameter has a schema description, but the others lack context. The description should compensate for the sparse 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 lists workflows assigned to a user or team, with filtering by status and type. It uses a specific verb (List) and resource (workflows), distinguishing it from siblings like is_get_workflow (single workflow) or is_advance_workflow (modification).
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 use for listing assigned workflows but does not explicitly state when to use this tool versus alternatives. For example, it does not mention that is_get_workflow should be used for a single workflow or that is_get_workflow_logs is for logs. Sibling tools are numerous, so guidance would be helpful.
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 must convey behavioral traits. It mentions returning quote and changes but omits details on side effects, authorization needs, or whether the renewal is final or a simulation. This is insufficient for a mutation 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?
Two sentences, no redundancy, front-loaded with action and outcome. Every word earns its place.
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 description covers the core purpose but lacks completeness for a mutation tool without annotations or output schema. Missing return structure, error conditions, and usage constraints. Adequate but not thorough.
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?
Input schema covers 100% of parameters with descriptions, so the description adds no extra meaning. Baseline score of 3 is appropriate.
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 'Initiate a policy renewal' with specific verb and resource, and uniquely identifies the tool among siblings. No other renewal tool exists, so differentiation is inherent.
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 for policy renewal but provides no explicit guidance on when to use or when not to use, nor mentions prerequisites or alternatives. It is adequate but lacks explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It states it uses an AI matching engine and returns ranked candidates with scores, but does not disclose side effects, authentication needs, rate limits, or behavior when no matches are found.
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 extremely concise with two sentences, front-loading the purpose immediately. No unnecessary words.
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?
For a tool with 4 parameters and no output schema, the description covers basic purpose but lacks details on pagination, sorting, error handling, or what 'match scores' entail. Minimal viable completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50%, and the description does not add meaning to parameters beyond the schema. It fails to explain parameter semantics or provide examples.
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 specific verb 'Find' and resource 'candidates for a specific job opening' with added value 'using IntelliMatch's AI matching engine', clearly distinguishing it from sibling `im_match_jobs_for_candidate` which does the reverse.
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 its usage (matching candidates to a job) but does not explicitly state when to use it versus alternatives like `im_match_jobs_for_candidate` or provide any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must cover behavioral traits. It mentions 'recent' logs but does not specify time window, log format, or whether the call is read-only. As a fetch operation, it is likely non-destructive, but details are lacking.
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 focused sentence with examples, no redundant words. It front-loads the purpose and key use case.
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?
Given no output schema, the description could describe the return format or structure. It also does not contextualize the three parameters or their defaults. The description is adequate but incomplete for a debugging 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 coverage is 67% (two parameters have descriptions). The description adds no additional meaning beyond the schema; it does not explain the 'lines' parameter or clarify enum values. Baseline score of 3 is appropriate.
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 it fetches recent application logs for debugging, specifying example log types (errors, matching engine events, API call traces). This distinguishes it from sibling tools focused on candidates, jobs, claims, etc.
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 use for debugging but does not explicitly state when to use this tool over alternatives or provide 'when not to use' guidance. Given distinct siblings, the purpose is clear, but usage boundaries are implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It correctly implies a read operation ('Get full details') but does not explicitly state that it is non-destructive, requires no specific authentication beyond normal access, or mention any rate limits or side effects. The listed fields give partial behavioral insight.
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?
A single sentence that efficiently conveys the purpose and included details. No wasted words. However, it could be slightly improved by front-loading the key action and structuring the list of details more explicitly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool with one parameter and no output schema, the description is fairly complete: it names the tool's purpose and enumerates key data fields. Missing are any notes on return format, error handling, or performance, but these are less critical given low complexity.
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 coverage is 100% (job_id with description 'Job requisition identifier'). The description adds the qualifier 'IntelliMatch job' which is already clear from the tool name, so it provides minimal additional semantic value beyond the schema. Baseline 3 is appropriate.
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 verb ('Get') and resource ('full details of an IntelliMatch job requisition') and enumerates specific included fields (requirements, compensation, hiring manager, open headcount). This distinguishes it from siblings like im_search_jobs or im_get_open_jobs_db which serve different purposes.
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 tells what the tool does but provides no guidance on when to use it versus siblings, e.g., im_search_jobs for searching or im_match_candidates_for_job for matching. Usage context is implied but not explicitly stated, leaving the agent to infer based on the tool's name.
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 full burden. It lacks behavioral details such as authentication requirements, scope of screenshot (full page vs viewport), or rate limits. This is a significant gap for a tool that performs an action.
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 sentence, concise and front-loaded with the key action and resource. No unnecessary words.
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?
Given low complexity (1 param, no nested objects, no output schema), the description covers the basic purpose but lacks behavioral context needed for an agent to safely and effectively use the tool. It is minimally adequate.
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 coverage is 100% for the single parameter 'page', which has an enum description. The description adds no additional meaning beyond what the schema provides, so baseline score of 3 is appropriate.
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 verb 'Take a screenshot', the resource 'IntelliMatch dashboard or a specific page', and the purpose 'for visual verification and sharing'. It effectively distinguishes from sibling tools like 'is_screenshot_workflow' by specifying the IntelliMatch domain.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for visual verification and sharing, which gives context. However, it does not explicitly mention when not to use this tool or compare with alternatives like 'is_screenshot_workflow', which would improve guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It correctly indicates a read operation (getting details), but lacks information on authentication, error handling, or rate limits. The description is adequate but not enriched beyond the obvious.
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, efficient sentence that front-loads the purpose and lists key fields. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (1 param, no output schema). The description covers what the tool does and what data it returns. It is complete enough for an agent to understand, though it could specify return format or handling of missing candidates.
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 coverage is 100% for the single parameter 'candidate_id'. The description repeats the parameter's purpose ('specific IntelliMatch candidate') but adds no additional semantics like format, examples, or constraints 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 uses a specific verb 'Get' and identifies the resource 'full profile details for a specific IntelliMatch candidate', listing included fields (work history, skills, certifications, match scores). It clearly distinguishes from sibling tools like 'im_get_candidate_db' and 'im_search_candidates'.
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 use when needing full profile details for a specific candidate, but provides no explicit guidance on when to use this tool versus alternatives (e.g., im_get_candidate_db, im_search_candidates). No when-not or exclusion criteria are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full disclosure burden. It indicates direct DB access and hints at performance impact ('when API limits are insufficient'), but does not specify authorization requirements, potential delays, or whether the query modifies data. The description is partially transparent but lacks details on costs or risks.
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 concise with two effective sentences. It immediately states the primary action and then provides usage guidance, with no redundant or unnecessary words. Perfectly front-loaded for quick understanding.
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?
Given the tool's complexity (direct DB query with 3 optional params, no output schema), the description provides a use case but omits details on return format, pagination behavior, or maximum result size. It is functional but not fully complete for an agent to anticipate all aspects of tool behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 33% description coverage (only 'skill' is described). The description does not explain the meaning of 'status' enum values, the effect of 'limit', or how 'skill' filtering works. It fails to add value beyond the schema, leaving the agent to infer parameter semantics from context.
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 verb 'Query' and the resource 'IntelliMatch database directly for candidate records.' It distinguishes from siblings by specifying use cases like bulk queries and custom filters, implying this tool is for direct DB access when API limits are insufficient.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises when to use the tool: for bulk queries, custom filters, or when API limits are insufficient. It implies that for simpler single-record queries, other tools (like im_get_candidate) should be used, though it does not provide explicit exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It explains this is a read operation and lists returned data, but lacks details on permissions, rate limits, data freshness, or response size.
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, focused sentence that front-loads the purpose and includes concrete examples of returned content, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple get tool with one parameter and no output schema, the description adequately lists what is included, though it could mention the structure or format of the response.
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 description does not add meaning beyond the input schema, which already describes the claim_number parameter with a format example. Schema coverage is 100%, so baseline 3 is appropriate.
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 it retrieves full claim details from iSuite and lists specific components (incident, adjuster notes, documents, payments, status history), distinguishing it from sibling tools like is_get_claims_summary_db and is_search_claims.
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 use when full details of a specific claim are needed, but it does not explicitly state when to use this tool versus alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It indicates a read operation ('Fetch'), but omits behavioral details like permissions, rate limits, or what happens if the workflow doesn't exist. Adequate but minimal.
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?
Single, focused sentence that immediately conveys the tool's purpose. No unnecessary words or repetitions.
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?
No output schema exists, and the description does not mention the return format or structure of logs. For a log retrieval tool, this is a gap. Adequate given the tool's simplicity, but could be improved.
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 description does not add information beyond the input schema, which already provides clear descriptions for all three parameters (workflow_id, level, lines). Baseline score of 3 is appropriate as schema coverage is high.
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 verb 'Fetch', the resource 'workflow processing logs', and the context 'debugging stalled or failed workflow instances'. It effectively distinguishes from sibling tools like is_get_workflow which fetches workflow details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies when to use (for debugging), but lacks explicit guidance on when not to use or alternatives among siblings. The use case is clear, but no comparison to similar tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It clearly implies a read-only operation and lists the possible stages. While it does not detail side effects or authorization needs, for a simple status retrieval tool, this is adequate.
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, well-structured sentence that front-loads the action and includes the key detail (list of stages). Every word earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema), the description provides the essential information: what it returns (pipeline stages) and the input needed. It lacks guidance on error cases or return format, but these are minor gaps for such a straightforward 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 coverage is 100% with one parameter (candidate_id) described minimally. The description adds no further meaning beyond the schema, such as format requirements or examples, but it is understandable in context. Baseline of 3 is appropriate.
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 ('Get') and resource ('recruitment pipeline status'), and explicitly lists the possible stages (applied, screening, interview, offer, placed, rejected), making it clear what the tool returns. It distinguishes from sibling tools like im_get_candidate or im_get_job by focusing on pipeline progress.
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 states the tool's purpose but provides no explicit guidance on when to use it versus alternatives. For instance, it does not contrast with im_get_candidate (which might return general candidate info) or im_get_match_score. The agent must infer usage from context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It correctly indicates a read operation (status and history), but does not disclose permissions, rate limits, or what 'history' precisely entails. It is adequate but not exhaustive.
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?
A single sentence that front-loads the verb and resource, with examples. Every word contributes to understanding. No unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple retrieval tool with one parameter and no output schema, the description covers the essential purpose and scope. It would benefit from a brief note on the output structure, but is sufficiently complete given the simplicity.
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% coverage for the single parameter workflow_id, so baseline is 3. The description adds no further detail beyond the schema's 'Workflow instance identifier'; it does not explain how to obtain the ID or any expected format.
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 explicitly states the tool retrieves 'current status and history' of an iSuite workflow instance, with concrete examples of workflow types (underwriting approvals, claim processing, compliance reviews). This differentiates it from sibling tools like is_list_my_workflows (listing) and is_get_workflow_logs (logs).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for obtaining detailed status/history of a single workflow, which is clear given siblings that list or advance workflows. However, it lacks explicit when-to-use or when-not-to-use guidance, such as suggesting is_list_my_workflows for overviews.
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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