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

With no annotations, the description carries full burden. It discloses many non-obvious behaviors: precedence rules, the single-condition SFQL restriction, the isNotFirstQuery dependency that makes last_modified_on act as a snapshot rather than a true delta, segment fallback, per-page file writing, and that only counts/paths are returned. This is exceptionally transparent.

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?

Though long, every sentence adds critical value. The structure front-loads purpose, then systematically covers filter precedence, datetime constraints, segment handling, and output behavior. No filler; it is dense but well-organized for a complex API.

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 complex tool with 8 parameters, no annotations, and an output schema, the description covers the key behaviors, failure modes, and return format. It even notes payload size (~80 KB) and page output. An agent has enough to call it correctly and avoid common pitfalls.

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. It explains person_id_external/user_id precedence, last_modified_on ISO format and limitations, select_segments default and fallback, and max_pages via 'capped by max_pages'. However, it leaves max_rows, company_id, and include_contingent_workers undefined. Partial but not complete compensation 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?

Description states a specific verb ('Query') and resource ('EC Compound Employee (SOAP) API') and adds the side effect of saving payload to disk. It clearly distinguishes from siblings like odata_query by naming the SOAP API, so an agent knows what this tool targets.

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?

Provides explicit guidance on when filters take precedence (person_id_external/user_id), the 3-month look-back constraint for last_modified_on, and warns about the isNotFirstQuery limitation. It does not explicitly name alternatives like odata_query for different APIs, but gives clear usage context for its own parameters and edge cases.

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 does so well. It states that the comparison runs remotely, not in the conversation, and details the return shape: in_sync, per-entity missing fields, and differing attributes as [value_in_a, value_in_b]. It even explains which attributes matter and why, giving the agent real operational insight.

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 front-loaded with the core purpose, then adds parameter behavior, then returns and edge-case meaning. Every sentence earns its place, including the size rationale and the sap: attribute explanation, without becoming bloated.

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 comparison tool of this complexity, the description covers what the tool does, how it behaves, what its main parameter controls, and what the output means. An output schema exists to supply formal return-field definitions, so nothing critical is missing for an agent to select and invoke the tool 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 0%, so the description must clarify the parameters. It fully documents the non-obvious entity parameter with an explicit example and the empty-string whole-service case, and it maps company_a and company_b to the two instances being compared. For three simple parameters, this is sufficient.

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 opening line states a precise operation—compare the OData configuration of two instances—and names the result: drift. It is clearly distinct from siblings like odata_query and odata_metadata, which are single-instance read/query tools.

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 clear guidance on entity scope: comparing one entity set versus the whole service. It also explains why the tool should run the comparison instead of doing it in-conversation, using a concrete size rationale. It does not explicitly name sibling alternatives or when-not-to-use cases, but the context is clear enough.

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 well by explaining the default-instance convention, the meaning of the 'plugins' and 'production' fields, and downstream refusal behavior. It could mention failure/authentication behavior, but for a zero-parameter listing tool the disclosed behavior is substantially transparent.

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 compact, front-loaded with the core purpose, and every sentence adds meaningful guidance. It avoids restating the tool name or schema and uses short paragraphs to separate purpose, usage, and field semantics.

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 zero parameters, an existing output schema, and sibling tools that depend on this tool's return value, the description fully covers what an agent needs: why to call it first, how company_id maps to other tools, and the meaning of returned fields that affect downstream behavior.

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 has no parameters and schema coverage is 100%, so parameter semantics are essentially moot. The description adds useful semantic context for the returned company_id field, especially the empty-company_id-means-default convention, which goes beyond the empty schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'List the SuccessFactors instances this server can reach.' This clearly distinguishes list_tenants from the sibling query/metadata tools, since its purpose is to enumerate reachable tenants rather than run queries against 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?

It explicitly says 'Call this first' and explains why: the returned company_id values are required by the other tools. It also names odata_query and ce_query and states that they refuse tenants with null production or config_error, giving concrete when-to-use and prerequisite context.

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 full burden and does so comprehensively. It discloses that entity='' fetches the entire large metadata, that the full map is written to a JSON file while a small map is returned inline, that navigation properties are resolved from the full service metadata when entity is given, that caching is per company_id per process, and that lookup failures are reported as warnings without blocking output. It also states it never loads raw EDMX into the conversation, which is valuable context.

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 somewhat long but well-structured: it starts with the core purpose, then explains entity behavior, output, navigation resolution, and finally usage guidance. Each sentence adds necessary information; there is no redundancy. While it could be tightened slightly, the length is justified given the tool's complexity and the lack of schema descriptions.

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 tool with two parameters, no annotations, and an output schema (likely containing return details), the description covers all necessary aspects: when to use it, parameter semantics, behavioral nuances (file writing, inline return, caching, error handling), and cross-references to sibling tools. It also explains why navigation properties are resolved from full metadata, making the tool self-contained for an agent.

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 explain parameters. It thoroughly explains entity: '' means whole service, 'EmpJob' means a specific entity set. It mentions company_id in the context of caching per company_id per process, implying it identifies the OData service endpoint, though it does not explicitly state its role as a service identifier. This is sufficient for an agent to infer usage, but a direct statement would make it fully clear.

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 clearly states the tool fetches OData $metadata (EDMX) and reduces it to a compact field map, with a specific verb and resource. It distinguishes itself from siblings like odata_query (querying data), compare_metadata (comparing), and list_tenants (tenants) by focusing on metadata retrieval and transformation. The entity parameter behavior is explicitly explained, leaving no ambiguity about 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?

The description provides explicit guidance on when to use this tool: it advises using navigation names from this tool to scope filters in odata_query instead of pulling whole entity sets locally. It also contrasts entity='' vs entity='EmpJob' usage, giving clear context for parameter choice. No explicit exclusions are needed since it directly references the alternative 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?

With no annotations, the description carries the full behavioral burden and does so thoroughly: it discloses __next pagination, orderby_added/paging_added, duplicate-key checks, truncation warnings, effective-dated slices, file writing, and preview size limits. This is far beyond a minimal mutation notice and gives the agent accurate expectations.

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 long but dense; the core operation is front-loaded and each paragraph addresses a distinct concern (paging, filtering, effective dates, warnings, file output). It could be tightened with headers, but it contains no filler or repetition.

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 complex tool with pagination, warning conditions, output files, and no annotations, the description covers nearly all invocation and side-effect details. The main gap is the undocumented `company_id` parameter and reliance on external server instructions for filter navigation.

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 20%, and the description compensates for most parameters: it explains `path` as an entity set carrying query options, `params` merge precedence and date controls, `max_pages` orderby/paging behavior, and `preview` inline-size constraints. `company_id` is never explained, so parameter documentation is not 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?

The opening sentence names a specific verb and resource: 'Run an OData v2 query...' and the second paragraph defines `path` as the entity set, so the tool's function is clear. It does not explicitly contrast itself with sibling tools like `ce_query` or `odata_metadata`, leaving some differentiation to the agent.

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 clear operational context: when to use max_pages, how to pass filters, how to select fields, and effective-date handling with fromDate/toDate. It does not name sibling tools or state when not to use this tool, so it falls short of explicit alternative routing.

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