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

Annotations already cover readOnly/idempotent/non-destructive; the description adds real value beyond them with a cost disclosure ('Costs 5¢ per call'), the vision-AI processing model, and the precondition that image_key must come from a prior read_datasheet response. It does not describe the response shape, though idempotency is confirmed by 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?

Dense and front-loaded with cost and core action before the routing guidance and example. Every sentence carries information, though the example and the parenthetical field explanations make it slightly longer than strictly necessary.

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, idempotent vision tool with no output schema, the definition covers purpose, cost, precondition, and selection guidance against its main sibling. What it does not state is the shape of the returned answer, which is the only material 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 coverage is 100%, so baseline is 3, but the description adds genuine meaning: image_key is described as 'the storage path in the image URL' sourced from read_datasheet, and question is framed as optional with an inline call example. This goes beyond the schema's terse field docs.

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 (reads with vision AI) and resource (datasheet image: graph, package drawing, pin diagram, schematic), and explicitly ties it to read_datasheet output. An agent can distinguish it from read_datasheet without opening either schema.

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 routes: 'For exact electrical values prefer read_datasheet's text tables; use this for what only the picture shows (package dimensions, pin assignments, curve trends, approximate values off a curve).' Both the when and the when-not, plus the named alternative, are stated.

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 safety profile (readOnly, idempotent, non-destructive, openWorld), so the bar is low, yet the description adds substantial operational context: a 2¢ per-call cost, duplicate-MPN quantity aggregation, the data sources used (exact identities, catalogue snapshots, distributor details), and epistemic caveats that 'unknown remains unknown' and single_source does not prove exclusivity.

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?

Cost is front-loaded, followed immediately by the checked dimensions, then input formats, then output-format behavior. Sentences are terse with no filler; even short clauses like 'No extraction.' carry exclusions rather than padding.

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?

No output schema exists, and the description still covers return shape (compact columns/rows by default vs verbose objects), input limits, accepted identifiers, aggregation behavior, data provenance, and cost. An agent has everything needed to invoke and interpret the result 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?

With 0% schema description coverage, the description must compensate, and it largely does: it explains that the mpn field accepts either MPNs or LCSC numbers, that duplicate MPNs aggregate quantities, and that verbose returns objects vs default compact columns/rows. It omits the qty bounds and the 20-item minimum (schema minItems=1), leaving minor gaps.

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 (check BOM lines) plus the exact scope of what is checked: lifecycle, stock vs quantity, single-distributor sourcing, datasheet status, stale/missing prices. This clearly separates it from siblings like check_design_fit, find_alternative, or check_extraction_status.

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 clear usage context: it operates on up to 20 BOM lines, accepts MPNs or LCSC numbers, and explicitly excludes extraction ('No extraction.'), routing that need elsewhere. It never names a sibling alternative (e.g. find_alternative for substitutions) or states when-not-to-use, so it falls short of full routing 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?

Adds substantial behavior beyond the annotations: a 5¢ per-call cost, extraction triggered for unread parts (uncharged), 1-3 minute latency estimates, the default 80% derating screening policy, junction-temperature estimation logic, and the rule that unmatched conditions hold the verdict at WARNING. These are exactly the operational traits annotations do not carry.

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 cost, purpose, and return values, and most sentences earn their place given the tool's genuine complexity. It is nonetheless long and sprawling, with the temperature and verdict details somewhat interleaved, so it falls short of ideal tightness.

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 10-parameter tool with no output schema, the description fully specifies the response shape (per-condition verdicts, margin_pct, derated_limit, limit_type, source_quote, unmatched, resolved_as, data_quality_warning, temperature_basis, status='extracting') and the screening policy. 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?

Schema coverage is 100% so the baseline is 3, but the description adds real meaning: which parameters apply to which part type, that efficiency is required for a switching converter's Tj estimate, and the Pd formulas per part type (LDO (Vin-Vout)×Iout, MOSFET ID²×RDS(on)). It stops short of documenting derating_pct's full interaction beyond what the schema already states.

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+resource (checks operating conditions against a part's ratings) and the exact return vocabulary (PASS/WARNING/FAIL/INSUFFICIENT_DATA per condition and overall). It also delineates scope against siblings with an explicit 'Not for' list naming analyze_image for SOA curves and excluding transient/thermal analysis and passives.

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 explicit when-to-use guidance by mapping required conditions to part type (converters/LDOs vs MOSFETs vs logic ICs) and provides a worked example call. The 'Not for' section names alternatives (analyze_image for SOA) and exclusions, so an agent can route correctly.

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 cover safety (readOnly, idempotent, non-destructive), and the description adds substantial behavioral context beyond them: it is free to call, expected latency is 1-3 minutes (5+ for large files or busy queues), timings are estimates not deadlines, and 'resolving' means the PDF is still being located. It also enumerates the fields returned per part state.

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 most important fact ('Free', scope of 1-20 parts), and nearly every sentence carries operational value. The middle section is dense and semi-colon heavy, which slightly reduces scanability but not substance.

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, so the description carries the burden and does so: it describes per-state return fields (current_step, elapsed_seconds, estimated_remaining_seconds, poll_after_seconds, error, related_ready). Combined with the polling and routing rules, an agent has everything needed to call and interpret it.

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% and the schema already documents the MPN/LCSC format and the 'not a value/designator' constraint. The description only restates the 1-20 bound, adding no format or syntax detail beyond the schema, so the baseline of 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?

States a specific resource (datasheet extraction status) for a bounded set of 1-20 parts, with the verb 'check' implied by the name. It distinguishes itself from siblings by explicitly mapping status values to the tools to use next (read_datasheet, prefetch_datasheets, request_datasheet_upload).

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?

Explicit when-to-poll rules: keep polling on 'resolving', 'queued', 'pending', 'extracting' (after poll_after_seconds); stop at 'ready', 'failed', 'no_source', 'unsupported'. It also routes each terminal status to the correct alternative tool, leaving nothing 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?

Annotations already declare read-only, idempotent, non-destructive, open-world, so the bar is lower; the description still adds real behavioral context beyond them: a 2¢ per-call cost, the all-or-nothing rejection of non-MPN inputs, the error column for valid-but-unknown MPNs, the resolved_as flag when a lookup lands on a different MPN, and the fact that it never itself starts extraction.

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?

Long but front-loaded: cost, then what it does, then the return shape, then the routing table, then the compact/mode behavior. Nearly every clause is actionable, though the routing enumeration is dense enough that it could be tightened 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?

No output schema exists, yet the description fully characterizes the return payload (per-provider specs, price breaks and links, cached datasheet summary, a parameters × parts table, and per-part lifecycle/stock/datasheet_status/resolved_as flags) plus the mode variants, so an agent knows both when and what to expect.

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; however the description adds meaning the schema does not, notably that include_summaries only applies with compact=true and that compact mode still retains the flags and comparison table. It restates the 2-5 bound and the 'not a value/reference designator' constraint that the schema already gives, so it does not reach 5.

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 ('Compares 2-5 parts ... side by side in one call') and explicitly scopes it to parts you already know, naming search_parts and find_alternative as the candidate-discovery siblings. An agent can distinguish this from every sibling without opening a schema.

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 explicit when-to-use and prerequisites: find candidates with search_parts/find_alternative first, prefetch_datasheets before expecting summaries. It also routes on results (datasheet_status 'ready' → read it; 'extracting' → poll check_extraction_status; 'not_extracted' → prefetch_datasheets; 'no_source' → request_datasheet_upload), which is unusually actionable.

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?

Goes well beyond the annotations: it discloses a 10¢ charge levied only on success, the fact that bytes are re-hashed and validated as a PDF containing the MPN, that the file is added to the organisation's private library, expected latency (1-3 min, up to 5+), and a full enumeration of failure codes with remedies. This is exactly the kind of cost/side-effect/timing context annotations cannot 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?

Front-loaded with the cost and the core action, then success payload, then failure modes. Dense but nearly every sentence carries operational value; the timing-estimate caveat sentence is the only slightly marginal addition.

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, the description still enumerates the success return fields (document_id, mpn, sha256, file_size_bytes, status='pending'), the follow-up tool, and all error states. Nothing an agent needs to invoke and handle the result 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?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning: it ties the token to its originating tool and documents a 15-minute validity window via the 'token_expired' failure, which the schema does not mention.

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+resource ('Confirms an upload started with request_datasheet_upload') and names the exact input to carry over. It is clearly distinguishable from siblings like request_datasheet_upload and check_extraction_status by identifying itself as the second step of the upload flow.

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 says when to call it (after request_datasheet_upload, pass its upload_token) and what to do next (poll check_extraction_status with the MPN). Failure branches even prescribe concrete retry actions, so the agent knows when to retry vs. abandon.

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?

Goes well beyond the readOnly/openWorld/idempotent annotations: it discloses the 2¢ per-call cost, that constraints are never relaxed silently, that failures surface in removed_by_constraints/relaxed_matches, that not_found is not charged, and that match_score is similarity not verified compatibility. This is exactly the behavioral context an agent needs before invoking.

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 cost and purpose, and nearly every sentence earns its place. It is dense and slightly sprawling toward the end (datasheet_status branching plus a pointer to server instructions), which keeps it just below a perfect score.

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, the description carries the return-shape burden and does so fully: match_score/match_notes, lifecycle/stock/datasheet_status, removed_by_constraints, relaxed_matches, and the not_found error with matches_without_constraints. An agent has everything required to call and interpret it.

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 already 100%, but the description adds real meaning beyond it: fuzzy package equivalence examples (SOT-23 ≡ SOT23, SOIC-8 ≡ SOP-8), the non-silent-relaxation semantics of constraints, and the compact/include_original_summary interaction ('leaves out the reference part's summary unless include_original_summary=true').

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+resource with the triggering conditions: 'Finds alternative and second-source parts for an MPN that is out of stock, end of life or too expensive.' It also explicitly differentiates from the sibling compare_parts ('for a head-to-head of candidates you already have, use compare_parts').

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?

Names when to use it (out of stock, EOL, too expensive) and routes to the alternative tool compare_parts for a different need. It even documents the downstream branching per datasheet_status, telling the agent which sibling to call next.

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/openWorld safety, and the description adds real behavioral context on top: evidence ranking semantics (pass/fail/unknown, unknown = unverified), source and test-condition retention, compact-vs-verbose return shape, 'Free' cost signal, and the DigiKey/JLCPCB configuration dependency. This is meaningful disclosure beyond the structured fields.

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 cheapest useful fact ('Free') followed by the core action, and every sentence carries distinct information about constraints, ranking, or output format. It is dense and the inline example fattens the prose, but nothing is truly 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?

For a 6-parameter filtering tool with no output schema, the description covers result ranking, evidence reliability, and return shape (compact vs verbose), which is what the agent most needs. Remaining gaps are minor: limit/pagination behavior and package filtering semantics are left to the schema defaults.

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 only 33% schema coverage, the description compensates well: it shows the constraints object shape via example, clarifies value format ('24 V', '10 mΩ'), mentions text operators like 'contains', and explains verbose toggles output objects vs compact rows. It omits semantics for limit (1-20, default 5) and leaves package unelaborated, so not quite a 5.

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+resource ('Find candidates by function and numeric specs') plus scope modifiers (units, package, stock) that differentiate it from a plain keyword lookup. It implicitly separates itself from check_design_fit via 'Does not validate design fit,' but never names search_parts or find_alternative, so sibling differentiation is incomplete.

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 a concrete usage example with query + constraints, explains what parameter naming schemes are accepted (vds, rds_on, canonical names, text constraints), and states a clear when-not boundary ('Does not validate design fit'). It stops short of routing the agent to alternatives like search_parts or find_alternative, so it is not a full 5.

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 readOnly/destructive/openWorld and cost, so the bar is lower, yet the description adds substantial context: cost ('Free', and prefetch 'at no extra charge'), MPN-mismatch disclosure via resolved_as/data_quality_warning, labelling of foreign prose in datasheet_summary, and a trust rule ('trust specialized_specs for numbers'). That is real behavior beyond structured fields.

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 highest-value facts ('Free. Full details for one part: ...') and every sentence carries information, including the status routing rules. It is a dense single paragraph with some repetition ('Free' vs 'no extra charge') and is long enough that a reader must scan carefully, which is a minor structural cost.

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?

No output schema exists, so the description carries the full burden of describing returns, and it does so by naming the key response fields (specialized_specs, datasheet_summary, datasheet_status, resolved_as, data_quality_warning). Combined with the status routing and the pointer to server instructions, an agent has everything needed to call and interpret this 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 schema already documents all five parameters and baseline is 3. The description nonetheless adds meaning: the effect of compact (family_summary vs labelled datasheet_summary), the no-extra-charge property of prefetch_datasheet, and the 'trust specialized_specs for numbers' guidance tied to compact. The provider enum and verbose are not elaborated in prose, keeping it short of a 5.

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+resource ('Full details for one part') and enumerates exactly what comes back: provider specs, price, stock/lifecycle, specialized_specs, datasheet summary, datasheet_status with available_sections. It also distinguishes itself from search_parts by positioning itself as the drill-down step, so an agent can tell it apart from sibling search 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?

Explicitly says 'Use after search_parts to drill into one result', then provides a status-to-alternative routing table ('extracting' → check_extraction_status, 'not_extracted' → prefetch_datasheets, 'no_source' → request_datasheet_upload). This is textbook when-to-use/when-to-use-something-else guidance naming the exact 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?

Goes well beyond the annotations (readOnlyHint/openWorldHint/destructiveHint) by disclosing timing expectations (1-3 minutes, 5+ for large or busy queue), that these are estimates not deadlines, the Free-plan daily cap ('trial_limit'), and the meaning of each status including that 'no_source' is only final after 'resolving' ends.

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 essential facts (free, up to 20 parts, returns immediately) and every sentence carries operational information. It is long and the status list partially repeats itself in the worked example, which costs it the top score.

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, the description fully covers the return contract: it returns immediately with per-part status and maps each status value to the next action. Combined with timing, cap, and failure-path guidance, an agent has everything needed 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?

Schema coverage is 100%, so the parameter is already documented, but the description still adds value by reinforcing 'MPNs or LCSC numbers only' and directing agents to resolve value-only BOM rows with search_parts first. That cross-tool guidance exceeds the schema's own note about not passing values.

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?

Opens with a specific verb and resource ('Starts background extraction for up to 20 parts at once') plus the key behavioral fact that it returns immediately. It clearly separates itself from siblings by naming check_extraction_status for polling and read_datasheet for the actual read.

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 explicit routing: poll check_extraction_status after prefetching, resolve value-only BOM rows with search_parts first, and use request_datasheet_upload when a part is 'no_source'. It also states the accepted identifier types (MPNs or LCSC numbers only), leaving little 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?

Annotations already declare read-only/non-destructive, but the description adds substantial behavioral context beyond them: a 2¢ per-call cost, that the first read triggers extraction and is not charged, 1-3 minute (or longer) extraction estimates flagged as estimates not deadlines, and the context-filling risk of section='all'. This is genuine disclosure an agent needs to plan around.

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?

Dense but front-loaded with the cost and the core read behavior before the mode details and status routing. Nearly every sentence carries actionable information, though the status→tool mapping could be compressed since it overlaps with server instructions.

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 read tool with no output schema, the description covers return-relevant signals (available_sections, next_offset, status='extracting'), continuation mechanics, cost, and failure routing. Nothing an agent needs to invoke 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?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning: section names come from the datasheet's own slugified headings, an unknown name returns nearest section names and pages, next_offset drives continuation, and query is only meaningful with mode='search'. This goes beyond the schema's field-level text.

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 ('Reads a part's datasheet') and immediately distinguishes two operating modes (section vs search). An agent can tell this apart from siblings like search_datasheets or get_part_details without opening a schema.

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 routes the agent: start with section='summary', use section mode for named sections, search mode for targeted questions, prefer specific sections over 'all'. It also maps every datasheet_status value to a concrete next tool (check_extraction_status, prefetch_datasheets, request_datasheet_upload), which is textbook 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?

Annotations only declare it is a write with no destruction; the description adds substantial non-obvious behavior: it is free but the fee is charged at confirm, validation rules (parseable PDF, declared hash/size, MPN on first page, image-only PDFs fail), 50 MB limit, org-private results, and 15-minute token expiry.

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 key cost fact ('Free.') and organized as a numbered three-step flow, so structure is strong. It is long and slightly repetitive (fee and checks restated), which keeps it from a 5, but almost every sentence carries actionable 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?

There is no output schema, yet the description enumerates the returned fields (upload_url, upload_method, upload_headers, upload_token) and the full two-step follow-through, so an agent has everything needed to complete the workflow.

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 already 100%, so the baseline is 3, but the description adds real meaning by explaining what the SHA-256 and byte size are checked against at confirm time and by clarifying the MPN-or-stem matching rule on page one. It does not add syntactic detail beyond the schema's examples.

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+resource: obtaining a signed URL to upload a datasheet PDF. It clearly distinguishes itself from siblings by naming confirm_datasheet_upload as the follow-up and by scoping to cases where the datasheet is absent.

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 explicit trigger conditions (datasheet_status 'no_source' after search completes, or 'unsupported'), an explicit exclusion (not while 'resolving'), and routes the agent to confirm_datasheet_upload as the required next step. Nothing is left 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?

Annotations already cover the safety profile (readOnlyHint, idempotentHint, openWorldHint, non-destructive), but the description adds genuinely new operational facts: a 2¢ per-call cost and the shape of the return ('matching chunks with part_number and a 0-100 relevance_score'). It does not discuss empty-result behavior or how limit affects result quality, which keeps it short of a 5.

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 sentences, all front-loaded: cost first, then scope, then return shape, then the alternative. Every sentence carries a distinct fact and none restates 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?

There is no output schema, yet the description still supplies the return shape and relevance scale, plus corpus scope, cost, and the disambiguation rule. An agent has everything needed to decide between this and search_parts and to interpret the 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 100%, so the baseline is 3, but the description adds real meaning for the required query parameter by showing the expected style (spec-based natural-language phrase rather than a part number). The limit and section_type parameters are left entirely to 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?

States a specific verb (semantic search) and resource (extracted datasheets) and immediately bounds the corpus with 'not every part that exists'. It also names the sibling it is not: search_parts for lookup by part number, so an agent can distinguish it without opening either schema.

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 the positive trigger with a concrete example ('low-noise LDO with PSRR above 70dB') and an explicit when-not rule routing part-number lookups to search_parts. Both the condition and the alternative are named, leaving nothing 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?

Annotations cover safety (readOnly, idempotent, openWorld, non-destructive), but the description adds substantial behavior the annotations cannot: queries run in parallel across three providers, results are merged by MPN, eval boards/kits are split out as related_kits for MPN queries, and private-library matches are appended with source='private_library'. That is exactly the kind of pre-call context an agent 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?

Front-loaded with the cheapest signal ('Free. Start here.') and then builds from core behavior to return shape to routing. The datasheet_status mapping is dense but each mapping earns its place; the trailing 'Full list in the server instructions' is the only mild redundancy.

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 carries the full burden, describing merged-by-MPN results with per-source price and stock, datasheet_status, related_kits and private_library entries with tags. Combined with the status-to-tool routing table, an agent has everything needed to call it and act on the 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 100%, with limit, query and providers all documented including the 3× multiplier caveat for providers='all'. The description adds no syntax or format detail beyond that, so the baseline 3 applies; it does implicitly hint that private-library results are appended regardless of the provider selection.

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 ('Searches components by part number or keyword') plus the exact scope (JLCPCB, Mouser, DigiKey, in parallel). It explicitly contrasts itself with the closest sibling: 'For spec-based discovery in plain language, use search_datasheets,' so an agent can route without opening any schema.

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?

'Free. Start here.' gives an explicit entry-point recommendation, and the description names the alternative (search_datasheets) with the condition that selects it (spec-based plain-language discovery). It further routes downstream work: poll check_extraction_status, prefetch_datasheets, request_datasheet_upload, each keyed to a concrete datasheet_status value.

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