IC datasheet search: parametric part finding, spec lookup, price comparison — grounded in real PDFs.
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Average 4.3/5 across 7 of 7 tools scored.
Most tools have clear distinct purposes: search finds corpus hits, get expands refs, get_image fetches images, get_specs reads canonical parameters, find_parts does parametric filtering, compare_parts evaluates distributor data. The main overlap is between search and lookup, but lookup is explicitly scoped to known parts and fuses search+get, making the distinction clear.
Naming is inconsistent: compare_parts, find_parts, get_image, get_specs follow verb_noun, but get, lookup, and search are bare verbs without a noun. This mix breaks the predictable pattern and makes the tool set feel less coherent.
7 tools is well-scoped for an IC datasheet server. Each tool covers a distinct aspect of datasheet access—search, retrieval, specs, images, parametric lookup, and comparison—without unnecessary redundancy.
The server covers the core datasheet workflows: searching the corpus, retrieving full sections, fetching images, reading canonical specs, parametric part selection, and distributor comparison. For a read-only datasheet resource, this is a complete surface with no obvious dead ends.
compare_partsAInspectRelative price tier, stock posture, and library class for a set of IC part numbers (a ranking from a distributor snapshot, not a live quote).
| Name | Required | Description | Default |
|---|---|---|---|
| parts | Yes | Two or more IC part numbers to rank relatively; needs >=2 priced parts for a verdict. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses that the result is a ranking from a distributor snapshot, not a live quote, which is valuable behavioral context. It doesn't explicitly state side effects (e.g., read-only), but the nature of the tool suggests no mutation.
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?
Single sentence, front-loaded with the output categories, no redundant words. It earns its place by adding the snapshot caveat.
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 one simple parameter and an output schema present, the description covers the essential purpose and the key caveat about snapshot vs live data. It could be more explicit about interpretation, but the schema and output schema likely handle that, so it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already fully describes the parts parameter (array of strings, needs >=2 priced parts). The tool description adds no additional parameter semantics, so baseline 3 applies given 100% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool produces a relative ranking (price tier, stock posture, library class) for a set of IC part numbers, distinguishing it from sibling tools like get_specs or find_parts. The 'not a live quote' caveat further specifies the data source.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when comparing multiple parts, and the schema adds a requirement of >=2 parts. However, no explicit when-not-to-use or alternatives are mentioned, so guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
find_partsAInspectShortlist IC part numbers by parametric spec constraints, ranked by how many constraints each part satisfies. Each constraint is {canonical, op: gte|lte|eq|range, value, unit?}. Returns per-constraint pass/fail/unknown. canonical MUST be one of the exact names below (case-sensitive):
abs_max_current, abs_max_voltage, adc_resolution, block_erase_time, breakdown_voltage_ds, channel_count, clamping_voltage, clock_frequency, cmrr, cmti, coil_power, coil_resistance, coil_voltage, collector_emitter_voltage, contact_current, contact_resistance, core_size, cpu_speed, current_rating, dark_current, data_rate, data_retention, dc_resistance, drain_current, driver_count, dropout_voltage, dynamic_range, eeprom_size, emitter_base_voltage, fall_time, forward_current, forward_voltage, gain_bandwidth, gate_charge, impedance, input_bias_current, input_capacitance, input_offset_current, input_offset_current_drift, input_offset_voltage, input_offset_voltage_drift, input_voltage_noise_density, insertion_loss, io_count, isolation_voltage, junction_capacitance, load_capacitance, memory_size, operate_time, operating_frequency, operating_temperature, output_capacitance, output_count, output_current, output_noise_voltage, output_power, output_voltage, output_voltage_high, output_voltage_low, page_program_time, peak_pulse_current, power_dissipation, program_erase_cycles, program_memory_size, propagation_delay, psrr, quiescent_current, ram_size, rds_on, receiver_count, rectified_current, release_time, response_time, reverse_leakage_current, reverse_recovery_time, reverse_transfer_capacitance, reverse_voltage, rise_time, sample_rate, sensitivity, settling_time, slew_rate, standby_current, supply_voltage_range, switching_frequency, switching_voltage, vgs_threshold, zener_voltage
| Name | Required | Description | Default |
|---|---|---|---|
| top_k | No | Maximum number of parts to return, best first. | |
| constraints | Yes | Spec constraints; a part is ranked by how many it satisfies. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It transparently discloses that results are ranked by how many constraints each part satisfies, returns per-constraint pass/fail/unknown, and that canonical names must be exact and case-sensitive. This gives a clear picture of the tool's core behavior, though it does not mention potential side effects or error handling.
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 primary purpose and then details the constraint format and return behavior. The long list of canonical names is necessary for correct usage and is structured as a comma-separated sequence. Every sentence earns its place, and there is no redundant prose.
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 complexity and the presence of a 100% parameter description coverage and an output schema, the description is largely complete. It explains the ranking, per-constraint result, and the canonical name list. It lacks explicit guidance on when to use this vs. sibling tools, but this is already captured under usage guidelines. Overall, an agent has enough context to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% parameter description coverage, so the baseline is 3. The description adds significant value by providing the full list of valid canonical parameter names, which is essential for using the tool correctly and is not fully spelled out in the schema (which references a resource instead). It also clarifies the constraint format in plain language.
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 'Shortlist IC part numbers by parametric spec constraints, ranked by how many constraints each part satisfies,' which clearly states the tool's purpose with a specific verb (shortlist), resource (IC part numbers), and unique ranking behavior. This distinguishes it from sibling tools like search or lookup, which likely do general searches, whereas find_parts is specifically for parametric constraint matching.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have parametric spec constraints, but it does not explicitly say when to use this tool over alternatives or when not to use it. There is no mention of compare_parts or search, so the guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
getAInspectExpand opaque ref tokens (from search/lookup) into full datasheet segment
content.
| Name | Required | Description | Default |
|---|---|---|---|
| refs | Yes | Opaque `ref` tokens from search/lookup hit records, to expand into full segment content. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the core behavior (expands tokens into full content) but does not disclose error handling, auth requirements, or side effects. The description paraphrases the schema without adding behavioral context beyond it.
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 a single sentence that is front-loaded with the verb and object, containing zero fluff or redundant 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?
Given the tool's simplicity (one parameter) and the presence of an output schema, the description adequately explains the tool's purpose and usage context. It does not elaborate on return structure, but the output schema covers that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% parameter description coverage, and the schema already explains that refs are opaque tokens from search/lookup. The description adds no new meaning beyond paraphrasing the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Expand' with a clear resource ('opaque ref tokens') and outcome ('into full datasheet segment content'). This distinguishes it from sibling tools like get_image and get_specs, which retrieve different data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly indicates this tool is for ref tokens originating from search/lookup, providing context for when to use it. It does not explicitly name alternatives or provide exclusions, but the context is sufficient for a simple tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_imageAInspectFetch datasheet figure images (PNG/JPEG) for opaque ref tokens obtained
from search/lookup/get. Returns each image plus its caption/description/page
metadata. Only type='image' segments have image data.
| Name | Required | Description | Default |
|---|---|---|---|
| refs | Yes | Opaque `ref` tokens (from search/lookup/get) of image segments; non-image segments return an error record. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and discloses important behavior: returns image plus caption/description/page metadata, non-image segments return an error record, and only image segments have data. It does not cover edge cases like partial array failures, but it provides solid transparency for a read-only fetch 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?
The description is two sentences, front-loaded with the action, and every clause adds value. It avoids repetition of schema details and uses compact, specific language.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema, the description covers input provenance, output contents, type constraints, and error behavior. It is sufficient for an agent to invoke the tool correctly and interpret results, making it complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single `refs` parameter, so the baseline is 3. The description adds meaningful context by explaining that refs are opaque, originate from specific tools, and that non-image segments produce error records, which goes beyond the schema's basic type definition.
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 ('Fetch') and names the exact resource ('datasheet figure images PNG/JPEG'), clearly distinguishing it from sibling tools like search or lookup. The mention of opaque `ref` tokens and the image type constraint makes the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states that refs come from search/lookup/get and that only type='image' segments have image data, providing clear context for when to use the tool. It does not explicitly name an alternative for non-image segments, but the implication is strong enough to guide correct usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_specsAInspectRead canonical extracted specs (min/typ/max, unit, conditions) for known IC part numbers. Optionally restrict to specific canonical parameter names.
| Name | Required | Description | Default |
|---|---|---|---|
| canonicals | No | Optional: restrict to these canonical parameter names; omit to return all available specs. | |
| part_numbers | Yes | Exact IC part numbers / MPNs, e.g. ['NE5532', 'LM358']. Family fallback is applied (e.g. TL084CDT -> TL084C). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It clearly identifies this as a read operation ('Read...') and specifies the kind of data returned (min/typ/max, unit, conditions). It also notes the optional filtering behavior, providing adequate transparency for a simple read 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?
The description is two sentences, front-loaded with the primary purpose, and every word adds value. It avoids redundancy with the schema while providing essential context.
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 tool's simplicity, the presence of an output schema, and a well-described input schema, the description is sufficient for an agent to know what to expect. It does not explicitly address behavior for unknown part numbers, but such detail is not essential for this read-only spec retrieval 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?
Schema description coverage is 100%, so both parameters are fully documented in the schema. The description adds minimal extra meaning beyond the schema (e.g., 'canonical' parameter names), but does not significantly compensate or add new semantics, matching the baseline for full coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reads canonical extracted specs (min/typ/max, unit, conditions) for known IC part numbers. This specific verb+resource combination distinguishes it from siblings like find_parts (searching) or compare_parts (comparison).
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 phrase 'for known IC part numbers' implies this tool is intended when you already have part numbers and need their specs, which is clear context for use. It does not explicitly name alternatives or exclusions, but the context is strong enough for an agent to infer when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookupAInspectOne-shot spec/section retrieval: full content of the most relevant datasheet
segments for one or more parts (fuses search+get). Already groups results per
part (via parts), so prefer lookup over search when you know the part(s).
| Name | Required | Description | Default |
|---|---|---|---|
| parts | No | Part numbers to scope and group the retrieval by; results are returned per part. | |
| query | Yes | What to retrieve, e.g. 'supply voltage' or 'absolute maximum ratings'. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and discloses key behaviors: it is one-shot, returns full content of relevant segments, combines search and get, and groups results by part. It doesn't mention side effects or limitations, but as a read-only retrieval tool, it provides sufficient transparency for safe invocation.
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 sentences achieve full purpose and usage guidance with zero fluff. Front-loaded with the core action, followed by actionable comparison to a sibling tool.
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 parameter set, clearly described schema, and presence of an output schema, the description is complete. It explains the tool's purpose, usage context, grouping behavior, and relationship to alternatives—everything needed for correct 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 covers 100% of parameter descriptions, so baseline is 3. The description adds minimal extra meaning beyond the schema, essentially reiterating that parts scope/group results and query specifies what to retrieve. No new semantic details are provided beyond the schema's own descriptions.
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 a specific verb+resource: 'one-shot spec/section retrieval' that returns 'full content of the most relevant datasheet segments' for parts. It also distinguishes from siblings by noting it 'fuses search+get' and is preferable to 'search' when part numbers are known.
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 recommends using 'lookup' over 'search' when the part(s) are known, naming the alternative and condition. The description also clarifies the grouping behavior ('already groups results per part'), giving the agent clear context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchAInspectSearch the datasheet corpus; returns hit records (metadata + snippet, each
with an opaque ref). Pass a ref list to get for full content.
For part-specific queries, pass scope='device:' (e.g. scope='device:NE5532')
to restrict hits to that part and avoid cross-part contamination.
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | Maximum number of hit records to return. | |
| q | Yes | Free-text query, e.g. 'input voltage noise density'. | |
| scope | No | Optional filter, e.g. 'device:NE5532', to restrict hits to one part and avoid cross-part matches. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the safety/behavior burden. It discloses the return shape (metadata + snippet with opaque ref), the chaining behavior (pass ref to 'get'), and the scope restriction behavior. It doesn't explicitly state read-only or side-effect-free, but 'Search' strongly implies it; the described behavior is safe and clear enough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact: three sentences, each earning its place. The first states the core action and result; the second explains the follow-up; the third details the scope option. No fluff, front-loaded with the primary purpose.
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 tool's moderate complexity and the presence of an output schema, the description covers all needed practical context: what the search returns, how to proceed to full content, and how to avoid cross-part contamination. It is complete for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the 'ref' opaque identifier and by giving a concrete example for 'scope' (device:NE5532) with a purpose. It also clarifies the query parameter's intent indirectly. This goes beyond the schema's field descriptions.
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 ('Search') and resource ('datasheet corpus'), then clarifies the return type ('hit records (metadata + snippet)'). It distinguishes from sibling tools by explicitly routing ref-based retrieval to 'get' and by showing scope usage that differs from part lookup.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit when-to-use guidance: it tells the agent to pass refs from search results to 'get' for full content, and provides concrete scope syntax ('device:NE5532') with a rationale ('avoid cross-part contamination'). This is direct, actionable context for selecting and using the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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