cg_example

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

With no annotations, the description bears full burden. It discloses that the tool is a curated dictionary, not free-form search; describes return formats for no pattern and with pattern; explains matching specificity-weighted logic; distinguishes entry kinds; and states verification status. It does not cover auth, rate limits, or error behavior, but is comprehensive for a read-only lookup tool.

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 somewhat long but every sentence adds value. It is front-loaded with purpose and then details behavior. Some redundancy could be trimmed, but overall well-structured for the complexity of the tool.

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

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

Given the absence of output schema and only two parameters, the description is thorough: it explains input behavior (no pattern vs pattern), output format (compact index or best match plus runners-up), matching algorithm, entry types, verification status, and a workflow. No significant gaps remain.

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

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

Schema description coverage is 0%, so the description must fully explain parameters. It does: 'pattern' is described in detail with matching behavior and examples (e.g., '1/sqrt' vs 'sqrt'); 'k' is explained as controlling the number of returned sources, with k>1 for composition. This adds rich meaning beyond the bare 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 it retrieves a verified C⏚ base for seeding and adaptation, contrasting with synthesizing from scratch. It explains it's a curated dictionary with scored lazy lookup, not free-form search. This verb+resource combination is specific and distinguishes it from sibling tools like cg_check, cg_simulate, etc.

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 a workflow (cg_example → edit → cg_check → cg_simulate → cg_generate_verilog → cg_synth) and advises against synthesizing hard kernels from scratch. It implies when to use this tool (to get a verified base) and when not to (when free-form search is needed), but does not explicitly name alternatives for specific tasks.

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