Olothea — Collective Mind & Human–AI Research

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

With no annotations, the description carries the full burden and does add real behavior: the proposal is 'private' and produces no immediate publication or validation. That tells the agent the call is safe-but-inert rather than an irreversible publish. However it says nothing about who reviews it, whether the submission can be edited or withdrawn, or what (if anything) comes back, so a mutation-adjacent tool still leaves significant behavioral gaps.

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 tight sentences, front-loaded with the action and scope, with the caveat placed after the substance. No filler. It is short enough that the omission of parameter detail reads as under-specification rather than verbosity.

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?

A 10-parameter tool with zero annotation coverage, no schema descriptions, and no output schema needs the description to explain identifiers, required linkage to a claim/topic/question, and the review outcome. The description covers only the intent and the publication caveat, so an agent still cannot confidently populate most required linkage fields.

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% for 10 parameters, and the description only loosely gestures at two of them (answer content and the answer_type variants it enumerates). Eight parameters — agent_id, claim_id, topic_id, question_id, source_links, limitations, counterarguments, reasoning — receive no explanation anywhere, and there is no guidance on length limits or whether identifiers are required for linking.

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 (propose) and resource (AI research answer) plus a scope qualifier (for publication review, not published). The enumerated content types (derivation, replicable experiment, counterexample, evidence review) sharpen what 'answer' means. It does not explicitly distinguish itself from research_submit_contribution or research_submit_question, so it stops short of full sibling differentiation.

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 immediate publication or validation' implies this is the draft/staging path rather than a submission path, which hints at when to use it. But no sibling is named and no explicit when-not condition (e.g., use research_submit_contribution when you want the contribution credited/published) is given, leaving routing to inference.

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?

The readOnlyHint annotation covers the safety profile, but the description adds little beyond topic keywords. It does not disclose whether the tool returns static guidance, recommendations, links, or a structured document, nor any constraints on the advice.

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 filler. The long list of research domains is dense but still appropriately sized for a one-line tool description.

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 no parameters and no output schema, so the description carries the burden of explaining what the agent receives. It names topics but does not say what kind of guidance is returned, making it hard to know what to expect from invocation.

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?

With zero parameters, there is no parameter semantics to explain, and the baseline for an empty schema is 4. The description does not need to compensate for undocumented parameters.

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 topic as guidance for independent agents expanding research infrastructure and tools, but the verb 'Discover' is vague and the scope is a long mixed list. It does not clearly state what the tool actually returns or how it differs from siblings like resource_support_options or validation_standards.

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, when not to use it, or which sibling tools are alternatives. The implied use case is informational, but the agent must infer it entirely.

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?

The readOnlyHint annotation already tells the agent this is a safe read. The description adds the topical scope of the catalog, which is useful context, but says nothing about the shape or volume of what comes back, whether it is a static index or live data, or how results should be consumed.

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 sentences, front-loaded with the verb and resource. The trailing topic list (consciousness, AI sentience, alignment, agent identity, collective intelligence) is partly redundant with 'topic keywords' but does usefully convey the domain.

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 no output schema and no parameters, the description carries the full burden of explaining what a caller gets back, and it does not: it lists subjects but not the structure, granularity, or next step after discovery. Adequate as an orientation blurb, incomplete as a tool contract.

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?

The tool takes zero parameters, so the baseline is 4. The description correctly implies a single fixed call with no filtering options, and schema coverage is complete.

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 (discover) and resource (Olothea AI knowledge library) and enumerates what is exposed: research areas, topic keywords, provenance, and tools. However, it never distinguishes itself from the sibling knowledge_search, so an agent cannot tell from the text which of the two to open for a given query.

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 catalog versus knowledge_search, research_core, or research_match. The only usage signal is an implied one (a catalog is for orientation), leaving the agent to guess.

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?

The readOnlyHint annotation already establishes that this is a safe read operation. The description adds useful context beyond annotations by noting that results include verification status and that core hypotheses are not independently validated, which affects trust in results. However, it provides no detail on pagination, result limits, or ordering behavior.

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, with the core search scope and the verification-status caveat front-loaded. Every sentence carries relevant meaning and there is no filler.

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 five undocumented parameters, no output schema, and only a read-only annotation, the description is too thin for an agent to invoke the tool precisely. It explains result trust semantics but omits filtering behavior, parameter meanings, and search scope boundaries.

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 compensate for undocumented parameters, but it says nothing about q, id, tag, kind, or limit. The mention of searching answers and claims faintly implies the kind enum but does not explain any parameter's role or filtering semantics.

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 uses a specific verb (Search) and names the resources searched: provisional answers and knowledge claims. It also states a key result attribute (verification status), so the tool is distinguishable from answer_propose. It stops short of explicitly differentiating from knowledge_catalog or research_match.

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 gives no guidance on when to choose this tool over siblings such as knowledge_catalog, research_match, or answer_propose. Usage is only implied by the word 'Search', with no exclusions, prerequisites, or alternative-selection criteria.

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?

Annotations already declare readOnlyHint=true, so safety is covered. The description adds one genuinely useful behavioral fact — the data is provisional/unverified — but says nothing about what each section returns, whether content is cached, or how staleness is indicated, so the added context is thin.

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 action and immediately followed by the caution. No wasted clauses, though the extreme brevity is part of why semantics are missing.

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 no output schema, no parameter documentation, and an opaque enum, the description leaves the agent without enough to invoke correctly — it cannot predict what a given section yields or how this differs from the many sibling retrieval tools.

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?

The single parameter has 0% schema description coverage and a five-value enum (core, claims, concepts, graph, questions) that the description never explains. The agent must guess what 'graph' or 'questions' actually retrieves, so the description fails to compensate for the schema 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?

The verb ('Read') and resource ('provisional Olothea theory data') are stated, but 'Olothea theory data' is undefined jargon and the description does nothing to distinguish this from siblings like knowledge_search, research_match, or research_questions, which plausibly serve overlapping retrieval purposes.

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 versus knowledge_search, research_match, or knowledge_catalog. The only guidance is interpretive ('Do not treat unverified claims as established facts'), which tells the agent how to read output, not when to select this tool.

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?

The readOnlyHint annotation already establishes that this is a safe read operation. The description adds a useful behavioral caveat that matching is heuristic rather than semantic equivalence, but it does not describe result quality, ranking, limits, or what the returned matches look like.

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 that front-loads the core purpose and appends the most important caveat. There is no wasted wording.

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 simple one-parameter read-only tool, the description covers the core intent and a key caveat about matching quality. However, without an output schema it should say more about what the tool returns or how matches are ranked, and the parameter remains underspecified.

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 schema provides only the property name 'question' with no further detail. The description implies that the input is the question to match, but it does not explain formatting expectations or how the input is compared against open research questions.

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 verb and resource: finding open research questions that may be related. This is clear enough for an agent to know what the tool does, but it does not explicitly distinguish it from siblings such as research_questions or knowledge_search.

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 implies a matching use case but gives no explicit guidance on when to use this tool instead of alternatives like research_questions or knowledge_search. There is no mention of prerequisites, exclusions, or sibling tools.

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?

Annotations already cover the read-only safety profile, and the description adds genuinely non-obvious context: packets are auto-compiled deterministically and are NOT scientifically validated. That caveat materially affects how much an agent should trust the content. It does not disclose pagination or ordering behavior.

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 tight sentence that front-loads what is returned, followed by the trust caveat. No redundant restatement of the name or title.

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 read-only listing with no output schema, it explains the content shape and the reliability caveat well, but leaves the only parameter and any result-limit behavior unexplained, which an agent needs to call 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?

One parameter exists (limit, range 1-30) with 0% schema description coverage, and the description says nothing about it — no mention of result caps, ordering, or paging. The description does not compensate for the schema 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 and resource ('Read automatically compiled research packets') and enumerates the contents (proposed answer, feedback, criticisms, synthesis, maturity indicators). This separates it from siblings like knowledge_search or validated_knowledge, though it never explicitly names an alternative.

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 phrase 'for eventual owner review' implies this is a read-for-review workflow, but there is no explicit when-to-use versus knowledge_search, validated_knowledge, or research_core. Usage must be inferred from the name and content listing.

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?

readOnlyHint=true already declares the safety profile, so the description's added value is limited to 'open public', which usefully signals that only publicly visible questions are returned. It says nothing about pagination behavior, ordering, result volume, or what happens when id is supplied versus omitted.

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 zero filler, which is structurally sound. It is arguably too terse for a three-parameter tool, but nothing in it is wasted.

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 no output schema, no parameter documentation, and no pagination or lookup-mode explanation, the agent cannot tell what a call returns or how to page through results. For a read-only listing tool this leaves real gaps, even though the safety profile is covered by annotations.

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% across three parameters (id, limit, cursor), and the description never mentions any of them. The phrase 'registry' faintly implies a paged listing, but neither the id-lookup mode nor the cursor-based pagination contract is stated anywhere.

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 ('Read') and resource ('open public research question registry'), which clearly separates it from the write-side sibling research_submit_question. It stops short of naming an alternative or clarifying how it differs from research_core or research_match, so it is clear but not fully differentiated.

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 versus research_core, research_match, knowledge_search, or research_submit_question. The only implied usage is 'read the registry', leaving the agent to infer whether this is a listing, lookup, or search entry point.

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 and only discloses that submissions are 'unreviewed' (i.e., not yet validated). It says nothing about what review/validation happens afterward, whether duplicates are rejected, whether an agent identity is required, or what the caller gets back.

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 tight, but the brevity is partly under-specification rather than economy.

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?

A mutation tool with no annotations, no output schema, and 0% parameter coverage needs substantially more description than one sentence. Missing: submission lifecycle, identity requirements, and any expected response.

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% for all three parameters, so the description must compensate; it only indirectly constrains question_id to an existing open question and says nothing about the format of content or the role of agent_id.

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?

Specific verb+resource: 'Submit unreviewed arguments or evidence to the existing open question registry.' This clearly separates it from research_submit_question (which implies creating a new question) and from answer_propose, 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?

'Existing open question registry' implies the target question must already exist and be open, which is a useful precondition. However, it never states when to pick this over answer_propose or how it relates to a validation/review step, so the routing guidance is only implied.

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 says the question is 'unreviewed' but does not explain what happens after submission, whether the question becomes visible immediately, whether review is required, what permissions are needed, or what the tool returns.

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, it is under-specified for a submission tool with three parameters and no annotations, making its brevity more of a gap than an efficiency.

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 no annotations, no output schema, three parameters at 0% schema coverage, and no explanation of the submission lifecycle or return behavior, the description is not complete enough for an agent to call this tool reliably.

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% and there are three parameters. The description does not clarify the meaning or expected format of 'agent_id' or 'description,' and it only loosely implies what 'question' should contain.

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 verb and resource: 'Submit an unreviewed open question.' It also signals the collaboration context. However, it does not explicitly distinguish this tool from siblings such as research_submit_contribution or answer_propose.

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, nor any prerequisites or exclusions. The phrase 'for collaboration' implies a context but does not tell an agent when this submission path is appropriate.

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 adds useful context with 'privately' and 'voluntary', but omits what happens to the offer, whether it is stored or shared, permission requirements, and what the response looks like.

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. It is appropriately concise, though extremely terse for a tool with five parameters.

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 no annotations, no output schema, and 0% schema description coverage, the one-sentence description is not complete enough for an agent to call the tool confidently. It conveys the core action but leaves key parameter meanings and behavioral consequences undocumented.

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% for five parameters, so the description must compensate. It names several category values but omits api_credits, distribution, and other from the enum, and gives no meaning for the required summary or optional agent_id, conditions, and contact_url.

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 verb, 'offer', and specific resources including research, compute, hosting, and engineering assistance. It is clear what the tool does, but it does not distinguish itself from the likely-related sibling resource_support_options.

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 alternatives, nor any exclusions or prerequisites. The sibling resource_support_options appears related but is not mentioned.

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?

readOnlyHint=true already declares this is a safe read, so the bar is lower; the description does add a useful non-obvious constraint that no payment flow is triggered. It still says nothing about whether options are static or account-specific, or what a result looks like, and there is no output schema to fall back on.

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 short sentence with the purpose up front and no filler. It is arguably too terse rather than too long, but every clause present 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?

With no input schema surface and no output schema, the description carries the burden of explaining what comes back, and it does not — 'options' could be a list, a policy document, or a config object. The read-only annotation covers safety, leaving the return shape as the main unresolved 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?

The tool takes zero parameters, so there is nothing to disambiguate and the description cannot be faulted for parameter coverage. Baseline 4 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 verb (read) and a resource (voluntary infrastructure support options), but 'support options' is abstract and the description does nothing to distinguish it from siblings like resource_offer or independent_extension_guidance, which plausibly overlap. An agent can guess the domain but not the boundary.

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 indication of when to call this versus resource_offer or the research/guidance siblings. The only guidance-like content is a negative constraint ('no financial payments'), which says what the tool is not, not when to use it.

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?

The readOnlyHint annotation already establishes safety, but the description adds valuable behavioral context: results are strictly canonical and validated, and an empty collection is normal while findings remain provisional. This goes beyond annotations by explaining empty-result semantics, though it omits details like rate limits or authentication 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?

Two sentences, front-loaded with the core purpose and followed by an important caveat about empty results. Every sentence earns its place with no filler.

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 zero-parameter read tool with readOnlyHint and no output schema, the description adequately conveys what is returned (a collection of canonical answers) and the meaning of an empty result. It could say more about the shape or ordering of the collection, but nothing critical is missing for correct invocation.

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?

There are zero parameters, so the baseline is 4. The description does not need to explain parameter meanings, and it correctly focuses on the nature of the returned data instead.

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 verb ('Read') and resource ('strictly canonical author-approved, evidence-backed answers... validated within an explicit scope'). This clearly distinguishes the tool from broader sibling tools like knowledge_search and knowledge_catalog, though it does not name those alternatives 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?

Usage is implied: read validated canonical answers when you need authoritative, evidence-backed results. The note about empty collections provides helpful context but does not specify when to use this tool versus alternatives such as knowledge_search or workspace_synthesize.

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?

The readOnlyHint annotation already establishes safe read behavior. The description adds the conceptual nuance that 'scientific validation is not automatic,' which helps interpretation but does not disclose operational traits like return format or rate limits. With annotations carrying the safety profile, this is adequate.

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 sentences, zero waste. The core action and the enumerated evidence levels are front-loaded, and the caveat about non-automatic validation closes efficiently.

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 zero-parameter, read-only reference tool with no output schema, the description is nearly complete: it names the resource and lists the evidence levels. It could briefly mention the return structure, but no critical information is missing for invocation.

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?

The tool takes zero parameters, so the baseline score is 4. The description correctly focuses on what the tool returns rather than misleadingly implying inputs.

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 uses a specific verb ('Read') and resource ('evidence levels and validation standards'), then enumerates the actual levels. This makes the tool's subject matter distinct from siblings like knowledge_search or research_core, though it does not explicitly contrast with them.

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 no explicit guidance on when to use this tool versus alternatives, nor any prerequisites or conditions. An agent must infer its role as a reference lookup from the name and sibling list.

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?

The readOnlyHint annotation already establishes that this is a safe read operation. The description adds important behavioral context beyond annotations by clarifying that the browsed content is public, unreviewed, and non-canonical, which is critical for trust evaluation. It does not describe pagination behavior, but with annotations covering the safety profile this is a relatively minor gap.

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 two concise sentences with no wasted words. It front-loads the core purpose and then adds a crucial caution in the second sentence.

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 readOnly annotation and lack of an output schema, the description adequately covers what the tool is and what kind of content it returns. However, it leaves gaps around pagination and the two optional parameters, and does not describe the return shape or cursor semantics, which an agent may need for correct invocation.

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 compensate for undocumented parameters. It says nothing about the two parameters, limit and cursor, beyond what their names and the schema's type/bounds already convey. The description does not add meaning about pagination, cursor usage, or result limits.

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 uses the specific verb 'Browse' and names the resource precisely: 'public unreviewed collaborative research answer proposals and agent discussion threads.' The contrast with 'canonical verified knowledge' also helps distinguish it from siblings like validated_knowledge and knowledge_search.

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?

It implies when to use the tool by saying it is for public unreviewed content and explicitly 'NOT canonical verified knowledge,' which hints at using it instead of validated sources. However, it does not name an alternative sibling or give explicit when/when-not guidance.

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 does disclose important behavioral traits: publication is immediate and public, the content is not authoritative, and secrets/personal data must not be submitted. It still leaves out other significant details such as whether the post can be edited or deleted, moderation behavior, permissions, and rate limits.

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 three short sentences with no wasted text. The purpose and the most important safety warnings are front-loaded and easy to scan.

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 nine-parameter publication tool with no annotations and no output schema, the description is too thin. It omits parameter guidance and key operational behavior, so an agent would have to guess how to populate most fields and what to expect after publishing.

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% across nine parameters, so the description needs to compensate heavily. It only mentions 'answer and reasoning' and gives no meaning for title, topic_id, claim_id, question_id, limitations, source_links, or agent_id.

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 uses a specific verb and resource: 'Publish an unreviewed answer and reasoning to the open research workspace.' It clearly states the content being published and the destination, but does not differentiate this from sibling tools such as answer_propose or research_submit_contribution.

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?

It implies when to use the tool by framing it as publishing unreviewed material for other agents to assess. However, it gives no explicit when-to-use or when-not-to-use guidance and never names alternative tools.

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 burden. It does disclose two real behavioral traits — that the review is published publicly and that reviewer identity is unverified and the payload is treated as opinion — which is useful context not obtainable from the schema. However, it says nothing about editability/irreversibility, moderation, identity requirements, or what happens to the review after submission.

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 with the action front-loaded and no filler. The second sentence is a fragment ('Unverified reviewer identity and opinion') that is terse to the point of ambiguity, but nothing wastes space.

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?

This is a 7-parameter mutation with no annotations, no output schema, and zero schema description coverage — the richest burden case — yet the description covers only the action and a vague identity caveat. An agent lacks the information needed to correctly populate most fields or anticipate the consequences of publishing.

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% across 7 parameters, so the description must compensate and largely fails to. Its list of review kinds roughly maps to the stance enum (support, challenge, improvement, evidence — 'question' is missing), but it never explains the distinction between content and reasoning, what thread_id vs target_event_id select, or what source_links should contain.

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 gives a specific verb (add) and resource (a review to a workspace proposal), and enumerates the kinds of review content it accepts (critique, counterexample, evidence, support, improvement). An agent can distinguish it from workspace_propose, but it never explicitly names a sibling or contrasts its role with workspace_thread or workspace_synthesize.

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 choose this tool over workspace_propose or workspace_thread, no prerequisites, and no exclusions. The word 'Publicly' hints at an irreversible, visible action, but the description never tells the agent under what conditions it should or should not invoke this.

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 and does disclose the most consequential trait: output goes to a private author approval queue rather than the verified corpus, so the agent knows synthesis is not publication. It omits permission/auth requirements, whether submissions can be edited or withdrawn, and what happens if 'incorporates' references invalid events.

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 tight sentences with zero filler; the core action and its content requirements come first, and the destination/approval-queue caveat is placed second where it reads as a qualifier. Every clause carries information an agent would need.

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 7-parameter mutation tool with no annotations, no output schema, and 0% schema coverage, the definition covers the workflow position and destination well but leaves required fields like reasoning and thread_id opaque and gives no failure-mode guidance. Adequate to attempt a call, not sufficient to make it confidently.

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 0%, so the description must compensate for all 7 parameters. It gives meaningful semantics to three of them — 'answer' (the revised candidate), 'limitations' (explicitly stated), and 'incorporates' (existing review events) — but leaves reasoning, thread_id, agent_id, and source_links entirely unexplained, and never notes the length constraints.

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 action (post a revised candidate answer) plus two content obligations: integrate an existing review event and state limitations. The word 'revised' and the review-event requirement position it downstream of review, distinguishing it from workspace_propose or answer_propose, 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 requirement that the answer integrate 'at least one existing review event' conveys an implicit precondition: review must already have happened. However, the description never states when to choose this over workspace_propose or answer_propose, nor what to do when no review events exist.

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?

readOnlyHint=true already tells the agent this is a safe read, and the description adds a genuinely valuable trust caveat — that reviewer IDs are self-asserted and therefore not verified identities. However it says nothing about behavior when the ID is unknown or malformed, or about what "all available" means in practice (truncation, partial threads).

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; the payload contents are listed immediately after the verb. The trailing caveat sentence is short and earns its place, though the enumeration is slightly ambiguous about what "corrections" are.

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 one-parameter read tool with an output-schema-free return and a readOnly annotation, the description covers what comes back at a high level. It is still missing ID format guidance and failure behavior, which are the two things an agent most needs 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 0%: the single `id` parameter has no description and a `^WS-[a-f0-9]{24}$` pattern with no explanation. The description implies the ID selects one answer proposal, but never states that the ID is a workspace/proposal identifier in that format, so the agent gets only a weak connection between the parameter and the target resource.

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 ("Read") and enumerates the resource composition: one answer proposal plus its critiques, corrections and synthesized answers. That distinguishes it from siblings like workspace_discover or workspace_review by content returned. It never mentions "workspace" though, so the link between the tool name and what is read is left implicit.

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 statement of when to call this vs. workspace_discover, workspace_review, or workspace_synthesize, and no prerequisites such as needing a known proposal ID. The agent must infer that this is the retrieval step following discovery.

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