공고 적합도 추천 (키워드·지역·단계·업종 기반 랭킹)

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

With no annotations, the description discloses key behaviors: automatic exclusion of closed announcements, fallback list and input-needed guidance for empty input, reliance on collected announcements with source and reference point, suitability as a reference only, no fabrication of facts, and marking missing items as '[Input needed]'. This provides rich 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.

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

The description is concise and front-loaded. In two short paragraphs, it conveys purpose, inputs, behavior, limitations, and fallback handling. Every sentence adds value without redundancy or 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?

Given no output schema, the description should explain the return structure. It mentions ranking score (0-100) and fallback list but does not specify the output format (e.g., list of grants with scores, fields included). Also, it does not mention how the 'limit' parameter affects results. The description is adequate but lacks output details for full completeness.

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

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

All 6 parameters have good descriptions in the schema (100% coverage). The description groups them as profile inputs but does not add significant extra meaning beyond the schema. It mentions ranking based on these inputs but does not detail how each parameter affects the ranking. Baseline score of 3 is appropriate since schema covers semantics adequately.

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 that the tool recommends store announcements ranked by suitability (0-100) based on entrepreneur profile inputs. It explicitly mentions keywords, region, stage, industry, and deadline as inputs, and distinguishes itself from siblings like find_grants by focusing on ranking and automatic exclusion of closed announcements.

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 explains the tool's functionality and when to use it: when you need a ranked list of suitable grants based on a profile. It mentions handling of empty input and automatic exclusion of closed announcements. However, it does not explicitly state when not to use this tool or suggest alternatives among siblings like check_eligibility or score_application.

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