GlobalSource Partners MCP Server

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context beyond this by listing response contents (metadata, biography, profile URL, visible sectors) and disclosing the generic not-found error for missing/inactive/non-author/hidden-sector cases. No contradiction with annotations.

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?

Four short, purposeful sentences with no redundancy. The first sentence states the core action, the second gives usage context, the third summarizes the response, and the fourth covers error behavior.

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 a rich output schema, so return values need not be fully spelled out. The description ably covers key output categories and error conditions, and the single parameter is fully documented in the schema. With strong annotations, this is complete for the tool's complexity.

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%, with the parameter description already explaining that author_uuid is a stable UUID from another research tool. The tool description reinforces this but adds no new semantics or format details beyond the schema, so baseline 3 is appropriate.

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+resource: 'Read a single research author by author UUID.' This clearly distinguishes it from sibling tools that read posts or search entities.

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?

Explicitly states when to use: 'when a user wants profile details for a specific research author returned by another research tool.' This also implies the author_uuid comes from a sibling tool, providing clear context and an implicit exclusion of search tools for already-known UUIDs.

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 and idempotentHint, but the description adds valuable behavioral detail: response contents (metadata, authors, sectors, body HTML, URL), permission-dependent teaser vs full content, and generic not-found error for missing/unpublished/hidden-sector posts. This goes beyond what annotations convey.

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?

Three short paragraphs, front-loaded with purpose, followed by usage flow, response summary, permission caveat, and error behavior. Every sentence contributes meaning without redundancy or fluff.

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 tool, the description covers the full context: how to select the UUID, what the response will contain, permission-based limitations, and error handling. No significant gaps remain.

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 only parameter post_uuid is fully described in the schema with the same guidance (use UUID from search_research_posts). The description adds no new semantic detail beyond repeating the schema. Baseline 3 for 100% schema coverage.

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 'Read a single published research post by post UUID', which clearly specifies the verb, resource, and scope. It distinguishes from sibling tools by explicitly referencing search_research_posts and focusing solely on reading a post rather than authors or entities.

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 when-to-use guidance: 'Use this tool after calling search_research_posts and selecting the most relevant result.' This directs the agent to a specific workflow and implies it is not for searching. Strongly differentiated from alternatives.

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 read-only, idempotent, and non-destructive behavior. The description adds value by explaining that results are ranked by match quality and include contextual fields, and reinforces that the tool does not modify data. This goes beyond the annotation hints without contradicting them.

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 structured into clear segments: main action, usage trigger, result format, and selection guidance. While slightly verbose, every sentence contributes useful context. It could be tightened by removing the redundant 'does not create or modify data' line, but it remains appropriately sized.

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 an output schema, so return values are covered. The description covers when to use it, what results look like, and how to choose the best match. It lacks details about default limit behavior, but that is in the schema. Overall, it gives enough context for correct use.

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?

Input schema provides 100% parameter descriptions, so baseline is 3. The description adds minimal extra parameter semantics, only implying that 'query' supports approximate names. It does not elaborate on 'limit' beyond the 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 clearly states the tool's purpose: search research entities by name or approximate name. It distinguishes itself from siblings like search_research_posts by focusing on entities broadly, and specifies that it returns UUIDs needed by other research 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 provides explicit guidance on when to use the tool ('whenever a user refers to an entity by name and a research entity UUID is needed') and gives detailed instructions on how to select the correct result from multiple matches. No exclusions are needed given the clear use case.

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 readOnly, idempotent, and non-destructive behavior. The description adds valuable behavioral context beyond annotations: it requires exact UUID values, forbids inference or transformation, and clarifies that results come from the selected research entities. This is meaningful added transparency even though annotations carry the safety profile.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than average but well-structured with a clear opening statement, workflow prerequisites, and concrete examples. Some redundancy exists (e.g., the required-field guidance is repeated in both description and schema), but every section earns its place by addressing a distinct operational concern.

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 that an output schema exists and all 7 parameters are documented, the description covers all necessary operational context: prerequisite entity lookup, exact UUID usage, filtering, ordering, and cursor-based pagination. An agent has everything needed to 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 coverage is 100%, so the baseline is 3. The description adds operational meaning beyond the schema by explaining the required workflow around research_entity_uuids, explicitly describing cursor reuse for next-page requests, and showing how filters and pagination fit together. This elevates it above the baseline.

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 ('Search published research posts') with a clear resource scope ('associated with one or more research entities'). It clearly differentiates from sibling tools like search_research_entities (searches for entities, not posts) and read_research_post (reads a single post).

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 explicit when-to-use guidance: require entity UUIDs, first call search_research_entities if only names are known, select the most appropriate entity, and never guess or transform UUIDs. It also provides examples covering multiple entities and pagination, making the intended workflow unambiguous.

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