Claimora

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

With no annotations, the description carries the full disclosure burden. It does reveal something useful about the return content (contradiction edges carry strength values), but says nothing about read-only nature, whether the operation is scoped to one claim, or what happens when no contradictions exist.

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?

One short sentence with no padding, but it is a noun phrase rather than a structured statement, so terseness comes at the cost of a verb and any routing guidance. Every word earns its place, but there are too few words to do the job.

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?

The tool is simple (one required param) and an output schema exists, so return-value explanation is not required. Still, the description is thin enough that an agent gains little beyond the name; a clause naming the claim scope and the read-only nature would close the gap.

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% and there is only one parameter, so the schema already explains claim_id fully. The description adds only the implicit framing that the claim is being inspected for contradictions, which is close to the baseline for a fully documented single parameter.

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 fragment identifies the resource (opposing claims / contradiction edges) and hints at returned data (edge strength), but supplies no verb telling the agent whether this retrieves, computes, or lists contradictions. It reads like a field docstring rather than a task statement. Distinguishable from siblings by topic, but the purpose is only implied.

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?

There is no statement of when to use this versus get_evidence, get_innovation_flags, or query_claims, and no prerequisites or exclusions. The agent must infer usage entirely from the tool name and the single claim_id parameter.

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?

With no annotations the description carries the full burden. It does disclose a real behavioral trait — excerpts are shortened by default and full_text lifts that — but says nothing about what happens when a claim has no evidence, how many excerpts come back, or any access constraints.

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?

Two short sentences, front-loaded with the resource and followed by the one behavior that changes output size. Nothing is padded or repeated.

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?

An output schema exists, so return values need not be explained, and the tool is a simple single-claim read. Still, with no annotations and multiple sibling 'get_*' tools, the description leaves the agent without routing guidance or edge-case context.

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 both parameters are already documented, and the schema's full_text text ('return stored excerpts without shortening') largely duplicates the description's 'excerpts are short unless full_text is true.' The description adds only a mild restatement of the default behavior.

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 resource and scope: provenance/evidence for exactly one claim, which distinguishes it from the claim-searching siblings (query_claims, search_smart). It lacks an explicit verb and never names a sibling, but an agent can tell what it returns without opening the schema.

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?

No when-to-use, no prerequisites, and no mention of alternatives such as query_claims. The only workflow hint ('Claim id returned by a search tool') lives in the schema, not the description, so the agent must infer the search-then-fetch-evidence pattern.

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?

With no annotations, the description carries the full behavioral burden, and it discloses only that results are ordered strongest-first. It says nothing about read-only nature, default page size (20), the 50-item cap, pagination via offset, or what 'strength' means or its 0-1 scale.

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?

A single front-loaded sentence with no filler. It is efficient, though the extreme terseness leaves meaning underspecified rather than over-written.

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?

An output schema exists, so return values needn't be explained, but for a 4-parameter filtered-list tool with no annotations, the description omits filtering semantics, pagination behavior, and any usage context. An agent could call it, but only by guessing at intent.

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 50%: only 'domain' and 'min_strength' have schema descriptions. The description's mention of 'strongest first' hints at the strength concept but never explains the scale, how min_strength filters, or how limit/offset paginate. It adds marginal value over the structured fields.

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 names a specific resource (innovation candidates produced by domain bots) and an ordering (strongest first), which is enough to distinguish it from siblings like get_contradictions, get_updates, or query_claims. The verb is only implied ('get' from the tool name), and no sibling is named explicitly, keeping it below a 5.

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?

There is no statement of when to use this tool, when not to, or which sibling to prefer for adjacent needs. An agent must infer the use case entirely from the resource phrase 'innovation candidates'.

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?

No annotations are provided, so the description must carry the full behavioral burden. It only states the update-time filter and omits pagination behavior, ordering, result limits, and whether this is a read-only operation.

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 a single front-loaded sentence with no wasted words. However, its extreme terseness contributes to missing usage and behavioral context.

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?

The tool has four parameters and an output schema, so return values are partly covered. Still, the description omits pagination behavior, ordering, domain filtering, and alternatives, which are important for an agent to invoke it 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 50%, with 'since' and 'domain' documented in the schema. The description restates only the 'since' filter and adds no meaning for 'limit', 'offset', or 'domain', leaving half the parameters without extra semantic guidance.

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 states a specific resource and selection condition: claims updated at or after a timestamp. It is clear what the tool returns, but it does not differentiate itself from siblings such as query_claims or search_smart.

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?

There is no explicit guidance on when to use this tool versus query_claims, search_smart, or other siblings. The timestamp filter implies a use case, but no alternatives or exclusions are named.

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?

No annotations are provided, so the description carries the full behavioral burden. It mentions ranking and returned fields but does not disclose that this is a read-only retrieval operation, how ranking works, pagination behavior, or any limitations.

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 a single compact sentence with no wasted words, and the key return fields are front-loaded. It is appropriately concise, though the concision masks missing usage information.

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?

The tool has five parameters plus nested filters (status, min_strength, assertion_type) and no annotations, yet the description says nothing about query semantics, filtering, or pagination. Although an output schema exists, the description remains too thin for a filtered search tool with multiple siblings.

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 80%, so the schema already documents most parameters, including query, limit, offset, and domain. The description adds no parameter meaning beyond what is in the schema, which is the baseline expectation at this coverage level.

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 identifies the resource as claims and lists returned fields (evidence excerpt, status, strength), but it is a noun phrase without a clear verb such as search, list, or rank. It does not distinguish query_claims from siblings like search_smart or get_evidence.

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?

There is no guidance on when to use this tool versus alternatives, no mention of prerequisites, and no exclusions. An agent must infer usage entirely from the tool name and schema.

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?

With no annotations at all, the description carries the full burden, and it does disclose one important behavioral trait: matching is text-only in this build, i.e. no semantic/vector recall. It says nothing about read-only safety, pagination behavior, result ordering, or the 50-result cap implied by the schema, so the disclosure is partial.

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?

Two short sentences, front-loaded with the purpose and followed immediately by the one caveat that matters. Nothing is padded or repeated.

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?

An output schema exists, so return values need not be spelled out, but with zero annotations on a search tool the description should still convey read-only nature, pagination expectations, and how it differs from query_claims. It covers purpose and one limitation and stops there.

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 only 50%: limit and offset have no descriptions, and the description adds no syntax, format, or semantics for any of the four parameters. The phrase 'natural-language question' merely restates the natural_language field's own description, so the text does not compensate for the coverage gap.

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 (Find) and resource (claims) and the input modality (natural-language question), so the agent knows exactly what the tool produces. It does not name or distinguish itself from the sibling query_claims, which appears to overlap heavily, leaving the agent to guess which to pick.

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?

There is no explicit when-to-use or when-not-to-use guidance and no mention of the alternatives among siblings (query_claims, get_evidence). The only selection signal is the implicit caveat 'This build matches text only', which hints the tool will not do semantic matching but never routes the agent anywhere else.

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