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

No annotations are supplied, so the description carries the full disclosure burden, yet it says nothing about whether the guide is returned as raw markdown text, whether it requires authentication, or how large it is. Calling it 'interactive' is unexplained and slightly misleading for a tool that presumably returns a static document.

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, which is appropriate for a simple fetch tool. The only cost is the dense product naming ('Synproof Syntropy Customer Implementation & Onboarding Skill') that crowds out more useful detail.

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 (no parameters, no nested objects, no output schema), so minimal description may suffice, but with no output schema the description should at least characterize the return value. It names SKILL.md without saying what the agent gets back or how to use it, leaving a meaningful hole.

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, which sets the baseline at 4, but the description adds no meaning about the returned artifact to compensate for the absent output schema. It is adequate for a parameterless fetch but does not go beyond the name.

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 concrete verb ('Return') and a specific resource ('Customer Implementation & Onboarding Skill (SKILL.md)'), which clearly separates it from the sibling read/verify tools. The purpose is identifiable, though the phrase 'interactive Synproof Syntropy ... Skill' is product jargon that adds little precision.

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 an agent should call this versus syntropy_verify, syntropy_list_worlds, or syntropy_get_world_axioms, and no indication of when it is unnecessary. The agent must infer that this is a documentation-fetch step, which is a notable gap given the four 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?

With no annotations and no output schema, the description carries the full burden; 'Retrieve' establishes this is a read-only fetch and 'all atomic ground-truth axioms' signals an unfiltered, complete return set. It does not disclose volume, pagination, ordering, or any permission requirements, which matters for a 'retrieve all' call against a document collection.

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 sentence with zero filler, front-loading the verb and the returned content before the input qualifier. It is efficient but arguably undersized, since some of the freed space could have been spent on routing to siblings.

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 read tool with no annotations and no output schema, the definition covers what is fetched and what identifier to supply. It leaves gaps around what an 'atomic axiom' looks like in the response, whether results are bounded, and how this differs from syntropy_verify on the same world.

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% and the single parameter is documented with both a handle example ('@pravo:obcansky-zakonik-cz') and the UUID alternative. The description only restates that a handle or ID is accepted, adding no format or resolution behavior beyond the schema; 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?

The description uses a specific verb ('Retrieve') and a clearly named resource ('atomic ground-truth axioms and metadata') scoped to a Small World handle or ID. An agent can distinguish it from syntropy_list_worlds (enumeration) and syntropy_verify (validation), though it never explicitly states those contrasts.

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 when-to-use or when-not-to-use guidance and no sibling tool is referenced, so the agent must infer that this is the fetch-by-identifier path rather than the verification or listing path. The only contextual clue is that it takes a handle or ID, which is a parameter fact rather than usage 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 must carry the behavioral burden, and it does disclose meaningful scope: only public and officially verified worlds are returned, so private or unverified content is excluded. However, it says nothing about pagination, result limits, ordering, no-auth-required status, or what a returned world record 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?

A single front-loaded sentence with no filler, which is appropriate for a simple list tool. The one weak spot is the unexplained '@domain:tag' notation, which adds ambiguity rather than value for a reader who hasn't seen the library.

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 two-optional-parameter read-only list tool with no output schema and no annotations, the description covers what is listed and its visibility scope, which is the essential part. It stops short of describing return contents, ordering, or result-size behavior, which an agent needs to plan a follow-up call 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 both optional parameters are already documented with examples. The description adds only the '@domain:tag' notation for world identity, which hints at the domain parameter's vocabulary but does not explain how 'domain' and 'search' interact or whether search covers tags.

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 ('List') and a specific resource ('public and official verified Small Worlds'), plus scope qualifiers that tell the agent this is a discovery/enumeration tool. Siblings like syntropy_get_world_axioms retrieve content for one world, so the distinction is inferable, though the description never names them 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 only implied: an agent can guess this is the entry point for discovering worlds before calling syntropy_get_world_axioms. There is no explicit 'use this when...' statement, no mention of prerequisites, and no guidance on choosing between the optional domain filter and keyword search.

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 discloses that inputs are sanitized as inert data and that there is a 'sceptical exhaustion limit,' which is useful, but it does not explain what a successful or failed trial returns, whether the process is expensive, or how the model/research settings affect behavior. For a complex verification tool with no annotations and no output schema, this leaves major 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?

The description is a single front-loaded sentence with a clear main action, but it is dense with proprietary jargon ('5-sieve Syntropy dialectical engine,' 'Synproof') that may not help an agent act. The sanitization note is useful but appended awkwardly. It is not bloated, but clarity suffers from the jargon.

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?

There is no output schema, and the description does not explain what the trial returns, such as a verdict, confidence, failure reasons, or structured findings. It also omits cost, latency, and whether external research is invoked by default in a way that affects results. For a complex, six-parameter verification tool with no annotations, this is incomplete.

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 input schema already documents every parameter, including world handles, model choices, max_failures, and initial_axioms. The description adds only the general framing of axioms and worlds, without additional syntax or edge-case semantics. Baseline 3 is appropriate because the schema does the heavy lifting.

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 action: putting a declarative claim, clause, hypothesis, or AI output on adversarial trial using a 5-sieve dialectical engine. This clearly distinguishes it from sibling tools that list worlds or retrieve axioms. The purpose is clear, though the Synproof/Syntropy jargon makes the exact verification outcome less concrete.

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 this is for verifying claims against axioms or a Small World, but does not explicitly say when to use it versus the sibling tools. There are no exclusions, prerequisites, or guidance on choosing between explicit axioms and a world handle. Usage is inferable but not fully guided.

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