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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds substantial behavioral context beyond those flags: it discloses that LEI/FIGI enrichment degrades gracefully when GLEIF/OpenFIGI are down, that ambiguous name matches return figi_candidates instead of asserting a single answer, that unresolved identifiers are stated explicitly under `unresolved`, and that internal cascading means calling this tool replaces 2-3 manual lookups. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is information-dense and front-loaded with examples and the primary use case, so an agent can quickly grasp the tool. However, it runs long, embedding edge-case details (e.g., non-equity instruments, ISIN examples) that, while valuable, could be trimmed or moved into separate guidance. Every sentence earns its place, but the length slightly dilutes the read.

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 2-parameter tool with no output schema, the description is complete: it covers input formats, resolution behavior, failure/degradation behavior, and even a citation format for drug lookups. It does not enumerate the full return shape, but the detailed discussion of fields such as `figi_candidates`, `unresolved`, and source labels gives strong context. The only gap is that it doesn't explicitly say what a fully resolved response object looks like beyond those fragments.

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%, so the baseline is 3. The description exceeds that baseline by giving detailed semantic guidance for the `value` parameter: for company it accepts ticker/CIK/ISIN/name, and for drug brand/generic. It also warns against including trailing security-class words for bond lookups and clarifies that an ISIN resolves to the legal entity that issued the security. This adds meaning beyond the schema's terse descriptions, though the `type` enum is itself straightforward.

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 opens with concrete natural-language examples ("What's the ticker for…", "find the CIK for…") followed by the core verb phrase "resolve a user-spoken NAME to the canonical/official identifiers other tools require as input." This gives a specific action on a specific resource, distinguishes it from siblings like entity_profile (identity resolution vs. profiling), and explains why the agent would reach for it first ("Use FIRST whenever you have a name but need an ID").

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 an explicit when-to-use directive: "Use FIRST whenever you have a name but need an ID." It also details which input formats are accepted (ticker, CIK, ISIN, company name, drug brand/generic) and, crucially, what to avoid ("never the question's full noun phrase"). It even explains fallback behavior across the two supported types, giving the agent clear routing context versus sibling tools that perform research, comparisons, or profiles.

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