resolve_entityMap an organization name (a contract recipient, a subsidiary, a PAC sponsor, a Form D issuer) to its public-company ticker. Exact normalized match against SEC legal names, aliases and the Exhibit 21 subsidiary map; returns the method and a confidence, or the reason for a miss. Never guesses.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Organization name as written |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only declare readOnlyHint and idempotentHint, which are a safety profile, not behavior. The description adds substantial behavior beyond that: the normalization strategy, the underlying data sources (SEC legal names, aliases, Exhibit 21 subsidiary map), and the return contract (method + confidence, or the miss reason). The 'never guesses' guarantee is a meaningful behavioral disclosure about failure modes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with zero waste: purpose is front-loaded, the entity-type enumeration justifies itself by scoping valid inputs, and 'Never guesses.' is a compact behavioral qualifier. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description covers the input domain, the matching strategy, and the return values (method and confidence, or the reason for a miss). Paired with the read-only/idempotent annotations, nothing an agent needs to invoke it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% ('Organization name as written'), so the baseline is 3. The description adds material meaning to the `name` parameter by enumerating what kinds of organization names are valid inputs and clarifying that the name will be matched via exact normalized comparison.
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?
States a specific verb ('Map'/'Resolve') and resource (organization name → public-company ticker), with an enumerated scope of accepted entity types (contract recipient, subsidiary, PAC sponsor, Form D issuer). The exact-match and 'never guesses' qualifiers distinguish it from the sibling search/PAC tools even though no sibling is named explicitly.
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 usage context is implied by the exact-normalized-match behavior and the 'never guesses' guarantee, which signals this is not the tool for fuzzy resolution. However, it never names a sibling alternative or states explicit when-to-use/when-not-to-use conditions; an agent must infer when to prefer this tool over the other lookup/search tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.