JsonMend

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

Goes well beyond the annotations: discloses the exact failure codes (unrepairable / no_json_found) via isError, the return payload contents, the no-network/no-storage guarantee, and the shared engine and limits with POST /api/v1/repair. That is the kind of mutation-free, idempotent behavioral contract an agent needs.

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?

Information-dense rather than padded: the repair list is front-loaded, then returns, then error behavior, then the purity/limits note. The long enumeration of repairs is justified by the breadth of the tool, though it makes the paragraph dense enough to be borderline.

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?

With an output schema present, the description does not need to explain return values in detail, yet it still names them. Combined with error semantics, idempotency, input limits and the purity guarantee, an agent has everything needed to call this correctly.

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 100%, so the schema already documents input, schema, coerce, draft and format with examples. The description adds only a light interaction note ('optional schema (+ coerce) also validates the result') and no syntax or format guidance beyond the schema, so the baseline 3 applies.

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 and resource (repair broken JSON text) and enumerates the concrete repair classes it performs, so the agent knows exactly what transformation happens. It does not explicitly name or contrast with the sibling validate_json, though the line about optional schema validation hints at the overlap.

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?

Gives clear context for when to reach for it (broken or messy JSON, e.g. an LLM reply with fences and prose) and states that valid input passes through unchanged, which tells the agent it is safe to call speculatively. It never explicitly routes the agent to validate_json for pure validation, so no exclusion is given.

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

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

Adds substantial context beyond the annotations: pure computation, no network access, inputs not stored, only local $refs resolve, remote $refs error, size limits, and that a schema mismatch is a normal result rather than an error. This goes well past the readOnlyHint/idempotentHint safety profile.

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?

Dense but front-loaded: the core action and error output come first, then input-vs-data, coerce, and limits. Every sentence carries information, though the final runtime/endpoint details add slight bulk.

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 an output schema exists, the description need not explain return values, and annotations plus text cover safety, determinism, limits, and repair/coerce behavior. An agent has everything needed to call this correctly.

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 structured fields already document all five parameters. The description still adds meaning by illustrating coercion directionality ('42' → 42, 5 → '5') and noting that changes are reported in a 'coercions' list.

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+resource ('Check JSON against a JSON Schema') and scopes the outcome ('every error with its JSON Pointer path'). It also distinguishes itself from the sibling repair_json by framing repair as a preliminary side effect rather than the purpose.

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?

Gives clear selection guidance between 'input' and 'data' and explains when to use 'coerce'. It does not name repair_json explicitly as the alternative for pure-fix workflows, but the usage context is otherwise unambiguous.

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