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 this is an arbitrary dispatcher, but says nothing about error behavior for unknown backend/tool names, whether mutating backend tools can be invoked through this path, or any auth/rate-limit considerations — meaningful gaps for a generic pass-through.

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?

Front-loads the core purpose in one line and follows with the Args block; nothing is padded. The reStructuredText double-backticks are cosmetic noise but negligible.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be explained. However, for a generic dispatcher the description should mention failure modes (invalid names) and whether destructive backend tools are reachable — neither is covered, which leaves an agent unequipped for edge cases.

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, and it does: each of the three parameters is explained, including the crucial provenance hint that backend and tool names come from list_sources(). It stops short of describing the shape or conventions of the arguments object beyond 'JSON object'.

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 — call any backend tool by name — and frames itself as the 'escape hatch' for features with no cross-source wrapper, which implicitly separates it from the sibling wrappers (search_papers, get_paper). It does not explicitly name which siblings to prefer, but the examples (openreview reviews, arxiv LaTeX source) make the niche 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?

Gives a clear condition for use (backend-specific features lacking a cross-source wrapper) and directs the agent to list_sources() to discover valid backend/tool names. It never states an explicit 'do not use when a wrapper exists', but that exclusion is strongly implied by 'no cross-source wrapper'.

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 behavioral burden. 'Fetch' implies a read-only operation and the source parameter explains auto-detection, but the description does not disclose authentication needs, rate limits, fallback behavior across backends, or failure modes.

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 action and then provides compact, useful parameter details. The Args block is efficient and every line adds information needed to call the tool correctly.

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 and has an output schema, so return values need not be explained. Parameter semantics are well covered, but the description lacks explicit routing guidance relative to sibling tools, leaving a small contextual gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for both parameters. It does this well: paper_id is documented with accepted ID formats (arXiv, DOI, OpenReview, Semantic Scholar), and source is explained as an optional backend override with auto-detection when omitted.

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: 'Fetch metadata / abstract for one paper by its id.' This clearly scopes the tool to a single-paper metadata lookup, which implicitly separates it from search_papers and get_paper_text, but it does not explicitly name or distinguish those siblings.

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 by the purpose: call this when you have a paper identifier and need metadata or an abstract. However, the description gives no explicit when-to-use guidance against siblings like search_papers or get_paper_text, nor any exclusions or prerequisites.

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 full burden. It does disclose that a network download happens, that text is truncated at max_chars, and that backend choice changes which identifiers resolve — genuinely useful behavior. It says nothing about permissions, rate limits, failure modes, or whether truncated output is signaled, which is a real gap for an unannotated tool.

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 purpose sentence followed by a compact Args block; every line carries information and nothing is padded. The docstring formatting is slightly awkward as agent-facing prose but costs no comprehension.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so the return value needs no explanation, and all three parameters are documented. What's missing is behavioral context an agent would want before calling: how errors surface for an invalid id, whether truncation is indicated in the response, and any cost or latency implications of downloading full papers.

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, and it largely does: paper_id is described as accepting arXiv ids, DOIs, or backend-specific ids, and source enumerates three concrete backend values that the schema itself does not declare as an enum. max_chars is explained as a truncation limit. It falls short of 5 because it doesn't clarify which id forms each backend accepts.

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 first sentence states a specific verb (download) plus resource (paper) and the returned artifact (readable text), which distinguishes it from the metadata-oriented get_paper. It never explicitly names the sibling it replaces, so an agent must infer the split from the return type alone.

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 source argument description implicitly guides selection by naming which backends handle which id types, which is useful context for choosing a call. However, there is no explicit statement of when to use this versus get_paper or search_papers, and no prerequisites or failure conditions are given.

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 load and does disclose the returned content categories, including 'missing credentials' — valuable context telling the agent it can detect unconfigured backends before attempting work. It doesn't state side effects, but a listing operation needs little more.

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, zero filler, with the payload description first and the call-ordering instruction second. Nothing could be removed without losing information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists and no parameters or annotations are needed, so the description only has to establish purpose and onboarding role — both are present. Nothing an agent needs to call it correctly is missing.

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?

Zero parameters, so the baseline is 4. There is nothing for the description to disambiguate and it does not waste words on nonexistent 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?

States a specific verb and resource ('List every paper source available through this server') and enumerates what the listing contains: capabilities, tool names, missing credentials. This is clearly distinct from siblings like search_papers or get_paper, which operate on papers rather than backends.

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?

'Call this first to decide which backend to use' gives explicit ordering and a decision purpose, which is strong guidance. It stops short of naming call_source_tool as the follow-on, but the routing intent is unambiguous.

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, yet it does not disclose that the pipeline generates and executes code (a significant side-effect/safety consideration), nor anything about runtime, cost, or whether artifacts are persisted. The stage list describes the internal flow, which is useful, but the risky behaviors of an execute-code pipeline are left unstated.

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?

Front-loaded with the core action and stage summary, followed by a compact Args block. The backend vendor mention ('sercantas Academic-Research-MCP-Suite') is of limited value to an agent selecting the tool, but it occupies little 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?

An output schema exists, so return values need not be explained, and both parameters are covered. However, for a tool that refines questions, generates code, and executes it, the absence of any note on duration, failure modes, or side effects leaves the behavioral picture 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 0%, so the description must compensate, and it does: 'question' is characterized as vague or broad (implying the refinement stage will handle it), and 'output_style' is given its allowed values (APA, ASA, generic) which the schema does not enumerate. Minor gap: 'generic' is unexplained and no format example is given.

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 ('Run the full academic research pipeline') and enumerates the four stages, so the agent knows this is an end-to-end multi-step operation rather than a single lookup. It does not explicitly contrast itself with the read-only siblings (search_papers, get_paper), but the 'full pipeline' framing makes the distinction inferable.

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: the argument hint 'A vague or broad research question' suggests this tool is for exploratory questions rather than targeted lookups, but there is no explicit 'use this instead of search_papers when...' statement or statement of prerequisites. No exclusions are given.

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 burden and does disclose real behavioral traits: defaults require no credentials, max_results is per-source (not a total), and year_from is only honored 'where supported' — a meaningful warning about uneven backend capability. It stops short of saying what happens when an individual source fails or whether results are merged/deduplicated.

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 scope statement is front-loaded and the credential note follows immediately; the Args block is compact. Mild redundancy in restating parameter names, but each line adds at least one fact not in the schema.

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 output schema covers return values, and the description covers defaults, credentials, and per-source limits. For a multi-backend aggregator, however, it is silent on partial-failure behavior and result merging across sources, which an agent would reasonably want before calling it with several backends at once.

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 and largely does: it names all four parameters and adds semantics the schema lacks — sources must come from list_sources() and can be omitted, max_results is per source, and year_from is best-effort. The 'where supported' caveat is genuinely useful; only the format of sources entries is left implicit.

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 first sentence gives a specific verb and resource plus scope ('across one or many sources at once'), which is unambiguous. It does not, however, distinguish itself from siblings such as get_paper or run_research_pipeline, so the agent must infer the boundary on its own.

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 rather than stated: 'Default sources are the ones that need no credentials' and 'Backend names from list_sources()' tell the agent how to activate non-default sources, but there is no explicit when-to-use versus get_paper/run_research_pipeline guidance and no when-not statement.

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