sheetsdata-mcp

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

Annotations declare readOnly/idempotent/destructive status, so the safety profile is covered. The description adds crucial operational context: vision AI's reliability limitations for precise numeric values, the dependency chain (image_key comes from read_datasheet output), and what types of visual content it handles (graphs, schematics, diagrams). It also implies idempotency through the examples showing consistent analysis.

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?

Excellent structure: purpose statement first, specific usage conditions second, important limitations highlighted with 'IMPORTANT:' callout, concrete examples last. Every sentence advances understanding. No tautology or repetition of schema fields. Length appropriate for a tool with complex sibling dependencies.

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 no output schema exists, the description adequately implies return value through examples ('classifies and describes the image'). The sibling relationship with read_datasheet is fully explained. Minor gap: could explicitly state output format (structured text vs raw description), but examples provide sufficient clues for an agent to proceed.

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 100% schema coverage, the baseline is 3. The description adds value by explaining the semantic relationship between tools: image_key is 'from the read_datasheet response (the storage path in the image URL)'—critical context for correct invocation. It also clarifies the question parameter's purpose ('to focus the analysis') with concrete examples showing how to formulate technical queries.

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 precise action statement ('Analyze an image from a component's datasheet using vision AI'), identifying the specific resource (datasheet images) and mechanism (vision AI). It clearly distinguishes from sibling tool read_datasheet by stating exactly when to use each: analyze_image is for when read_datasheet 'returns a section containing images.'

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 conditions ('Use this when read_datasheet returns a section containing images') and when-not-to-use guidance ('IMPORTANT: For precise numeric values... prefer read_datasheet text tables first'). Lists specific appropriate use cases (package dimensions, pin assignments, graph trends) and explicitly names the alternative tool for text data.

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 context beyond annotations: explains it checks 'absolute maximum ratings and recommended operating conditions' (specific validation logic), and discloses output format ('PASS/FAIL/WARNING per parameter with margins') which compensates for missing output schema. Consistent with readOnlyHint=true.

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 efficiently structured sentences: purpose upfront, methodology, output format, and concrete example. Every sentence earns its place with zero 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?

Despite no output schema, description fully explains return structure (pass/fail/warning with margins) and validation logic. Example provides complete usage pattern. Adequate for a 7-parameter validation tool.

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?

Baseline is 3 given 100% schema description coverage. The concrete example invocation ('TPS54302', input_voltage=24...) adds practical context for how electrical parameters map to real component validation, elevating above 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?

Specific verb (validate) and resource (component against operating conditions) clearly stated. Distinguished from siblings like 'get_part_details' (retrieval) or 'compare_parts' (comparison) by focusing specifically on design validation against datasheet ratings.

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?

Implies usage context through 'operating conditions' and design parameters, but lacks explicit guidance on when to choose this over 'get_part_details' for simple spec lookup or how it relates to 'read_datasheet'. No alternatives or exclusions named.

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 profile (readOnly, idempotent, non-destructive). Description adds substantial value: enumerates all possible status values with definitions ('ready', 'extracting', etc.), discloses additional return fields (step, elapsed time, document ID), and clarifies cost. Does not mention rate limits or polling frequency recommendations, preventing 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 sentences, perfectly structured: purpose → return values → additional fields → usage guidance. Every sentence carries unique information. No redundancy with schema or annotations. 'Free' prefix in final sentence efficiently signals cost before giving workflow instruction.

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, but description compensates by detailing the status enum, tracking fields, and workflow context. Annotations are present and clear. Could be improved by mentioning error handling or rate limiting for the polling pattern, but adequate for tool selection and invocation.

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 clear descriptions ('List of MPNs to check (max 20)'). Description mentions 'one or more parts' which aligns with the parameter but adds no semantic detail beyond what the schema already provides. Baseline 3 is appropriate given complete schema documentation.

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?

Clear specific verb ('Check') + resource ('extraction status of one or more parts'). Explicitly distinguishes from siblings 'prefetch_datasheets' and 'read_datasheet' by stating this polls progress after those operations, establishing the workflow relationship.

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-use guidance ('use this to poll progress after prefetch_datasheets or read_datasheet') and cost signal ('Free'). Names specific sibling tools as workflow predecessors, making the orchestration pattern 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?

Excellent context beyond annotations: discloses data sources ('merged details from all providers'), caching behavior ('cached datasheet summaries'), and specific return fields ('datasheet_status', 'specs, pricing, availability'). Complements readOnlyHint by explaining what data is returned without contradicting safety 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?

Three efficient sentences followed by a concrete example. Front-loaded with critical constraints (2-5 parts). No redundancy—each sentence adds distinct information (purpose, data sources/return value, specific fields).

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?

Complete for a single-parameter comparison tool. Compensates for missing output_schema by describing return contents (merged provider data, specs, pricing, availability). Safety profile covered by annotations. Minor gap: doesn't mention error cases (e.g., invalid part numbers).

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 100% schema coverage, baseline is 3. The description adds value by reinforcing the '2-5' constraint in natural language and providing a concrete example (['TPS54302', 'LM2596', 'MP2359']) that clarifies expected input format beyond the schema's description.

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?

Clear specific verb ('Compare') + resource ('electronic components') with explicit scope ('2-5', 'side by side'). Distinguishes from siblings like get_part_details (single part) and search_parts (discovery) by emphasizing comparison of specific known part numbers.

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?

No explicit 'when to use vs alternatives' guidance, but implies usage through the '2-5 specific part numbers' constraint and concrete example. Lacks explicit guidance like 'use search_parts to find parts first, then use this to compare specific candidates'.

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 the safety profile (readOnly, non-destructive, idempotent) and open-world nature. The description adds valuable behavioral context not in annotations: it specifies the search logic ('similar specs, same package, or compatible pinout') and confirms the cross-provider/external search implied by openWorldHint.

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 tight sentences followed by a concrete example. Information is front-loaded (purpose → mechanism → usage context), with zero redundancy. Every sentence earns its place without verbosity.

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 the simple 3-parameter schema, strong annotations, and lack of output schema, the description provides sufficient context: clear purpose, usage scenarios, matching criteria, and an example call. A minor gap is lack of indication about return value structure, though this is acceptable for a read-only search tool of this 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?

With 100% schema description coverage, the schema already fully documents all three parameters including the constraints syntax. The description provides a concrete usage example, but this illustrates rather than adds semantic meaning beyond the comprehensive schema documentation. Baseline 3 is appropriate given the schema carries the burden.

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 specific action ('Find alternative/substitute components') and resource (components for a given part number). It distinguishes from siblings like 'search_parts' (general discovery) and 'compare_parts' (side-by-side comparison) by emphasizing the substitute-finding use case and cross-provider search scope.

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 clear 'when to use' guidance ('Useful when a part is out of stock, too expensive, or you need a second source'). However, it does not explicitly name sibling tools as alternatives (e.g., when to use 'compare_parts' vs this tool), which would provide complete 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 establish read-only safety (readOnlyHint: true, destructiveHint: false). The description adds substantial behavioral context beyond these hints: specific return fields (specs, pricing, stock, datasheet_status with enum values 'ready'/'extracting'/'not_extracted', available_sections), and the side-effect behavior of prefetch_datasheet ('trigger background extraction', 'no extra charge').

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 dense paragraphs with zero waste. The first sentence establishes purpose and return value; the second covers workflow context; the third paragraph provides critical input validation constraints. Every sentence conveys unique information not duplicated in the schema or annotations.

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?

Despite lacking an output schema, the description comprehensively details the return structure (specs, pricing, stock, datasheet summary, status fields) and behavioral states. Deducting one point only for lack of error handling or rate limit disclosure, which would be necessary for a complete 5.

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 100% schema description coverage, the baseline is 3. The description adds significant value by providing expanded positive examples ('TPS54302DDCR', 'STM32F446RCT6') and explicit negative constraints ('100nF', '10K', 'buck converter') for part_number that clarify the expected input format beyond the schema's basic example.

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 specific action ('Get full details'), resource ('electronic component'), and key inputs ('manufacturer part number or LCSC number'). It explicitly distinguishes from sibling tool 'search_parts' by stating 'Use after search_parts to drill into a specific result,' establishing the tool's position in the workflow.

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 ('Use after search_parts to drill into a specific result') and clear when-not-to-use guidance ('Do NOT pass bare component values... descriptions... or reference designators'). The negative examples ('100nF', 'R1') prevent common misuse patterns, effectively differentiating from the broad search capability of 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?

Beyond annotations (readOnlyHint=true), description discloses non-blocking behavior ('returns immediately'), specific return statuses ('ready', 'queued', 'error'), and side effects ('extraction started'). Missing rate limits or retry behavior prevents 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?

Well-structured with logical flow: purpose → return values → example → critical warnings. Bullet points efficiently convey validation rules. Slightly verbose but necessary given the complexity of MPN validation requirements.

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

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

For a single-parameter tool with full schema coverage and no output schema, description is comprehensive: covers functionality, return semantics, workflow integration, input validation edge cases, and handling of passives without MPNs.

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?

Despite 100% schema coverage (baseline 3), description adds substantial semantic value with concrete examples of valid MPNs ('STM32F446RCT6') vs invalid inputs ('100nF', 'R1', 'LED red'), and workflow guidance to use search_parts first if MPNs are missing.

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 uses specific verb 'Trigger background datasheet extraction' with clear resource scope 'multiple parts at once (up to 20)' and distinguishes from sibling read_datasheet by explaining this is for warming up cache before reading.

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 ('warm up datasheets for a BOM before calling read_datasheet') and names specific sibling alternatives (search_parts for finding MPNs, read_datasheet for actual reading). Detailed negative constraints specify when NOT to use (bare values, reference designators, descriptions).

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 crucial behavioral details beyond annotations: mentions extraction latency (30s-2min) and caching behavior, explains automatic fallback from section mode to search mode when section not found, and notes that section names are dynamic. Annotations only cover read-only/destructive hints; description supplies operational timing and fallback logic.

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?

Well-structured with clear visual hierarchy using bold headers for modes. Information-dense without redundancy. Front-loaded with core purpose, followed by mode explanations, workflow guidance, and input constraints. No wasted words.

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?

Comprehensive for a read operation with no output schema. Covers both modes thoroughly, explains the extraction/caching lifecycle, and documents expected latency. Could briefly mention return format (string vs object), but completeness is strong given the schema and annotations provided.

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 has 100% coverage (baseline 3), but description adds significant value with concrete examples: section names ('register_map', 'i2c_interface'), query examples ('charge voltage register'), and valid/invalid part_number formats ('TPS54302' vs '100nF'). Also explains the workflow logic (start with summary, then specific sections).

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?

Clear specific verb ('Read') and resource ('component's datasheet'). Distinguishes two operational modes (section vs search) and implicitly differentiates from sibling tools like 'search_datasheets' (which likely searches across parts) by emphasizing this reads from a specific part's datasheet.

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?

Excellent explicit guidance: instructs to 'Start with section='summary' to get an overview', states when to use search mode ('Best for targeted questions...Use when you need to find specific information'), and provides explicit negative constraint ('Do NOT pass bare component values'). Includes latency expectations for first calls.

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 profile (readOnly/destructive), while description adds critical behavioral context about data availability ('Only searches datasheets that have been previously extracted — not all parts that exist'). Does not contradict 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?

Five sentences efficiently cover purpose, methodology, use case with example, scope limitations, and sibling alternatives. No redundant information; every sentence earns its place and content is well front-loaded.

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 3-parameter search tool with full schema coverage and annotations present, the description adequately covers scope constraints, search methodology, sibling differentiation, and practical examples without requiring output schema documentation.

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 100% schema coverage, baseline is 3. Description adds value by providing a concrete example query that illustrates the expected natural language format for technical specifications, enhancing understanding of the 'query' parameter semantics beyond the schema's generic description.

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 clearly identifies the action (semantic search), resource (extracted datasheets), and differentiates from siblings by contrasting with search_parts for specific part number lookups.

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 ('Best for broad spec-based discovery'), provides a concrete example query ('low-noise LDO with PSRR above 70dB'), and explicitly names the alternative tool for different use cases ('use search_parts instead').

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 valuable execution context beyond annotations: explains parallel provider querying, result merging logic ('merged by MPN'), and output field semantics ('datasheet_status'). Annotations cover safety profile (readOnlyHint, destructiveHint), so description wisely focuses on operational behavior rather than repeating safety metadata.

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?

Seven sentences with zero redundancy: purpose statement, entry-point claim, technical execution details, result structure, output field documentation, usage examples, and sibling differentiation. Information density is high with optimal front-loading.

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?

Despite lacking an output schema, the description adequately compensates by documenting key result fields (datasheet_status, merged pricing/stock) and multi-provider behavior. Covers all necessary invocation context for a search tool with good input schema coverage.

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 100% schema coverage (baseline 3), the description adds substantive value through concrete query examples ('STM32F103') and behavioral context ('Queries all configured providers in parallel') that clarifies the providers parameter's runtime behavior beyond the static enum 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?

The description opens with a specific verb and resource ('Search for electronic components'), delineates searchable fields ('part number, description, or keyword'), and explicitly distinguishes itself from siblings like search_datasheets ('For spec-based discovery... use search_datasheets instead') and hints at integration with read_datasheet via datasheet_status field.

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 entry-point guidance ('Start here — this is the best entry point'), clear alternative routing ('use search_datasheets instead'), and concrete query examples ('STM32F103', 'buck converter 3A') that clarify expected input patterns.

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