IC datasheet search: parametric part finding, spec lookup, price comparison — grounded in real PDFs.
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Average 4.4/5 across 7 of 7 tools scored. Lowest: 3.8/5.
Each tool maps to a distinct retrieval mode (parametric search, ranking comparison, corpus search, segment expansion, image fetching, canonical specs), but lookup/search/get_segments overlap somewhat in scope. The descriptions mitigate this by directing when to prefer lookup vs search.
Most tools follow a clear snake_case verb_noun structure (find_parts, compare_parts, get_image, get_segments, get_specs), with lookup and search as shorter imperative verbs. This is readable and predictable, though not perfectly uniform.
Seven tools cover the datasheet workflow without redundancy: search, lookup, specs, images, compare, and parametric find. The count is appropriate for a focused IC datasheet domain.
The tool set covers the full retrieval workflow from unknown-part corpus search to known-part spec lookup, segmented content, and figure images. Parametric search and part comparison add high-level selection capabilities, with explicit fallback guidance preventing 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, then by headroom past those thresholds, and spread across device families. Each constraint is {canonical, op: gte|lte|eq|range, value, unit?}. Returns per-constraint pass/fail/unknown, plus matched (parts that qualified) vs returned (parts in this response) — when they differ this is a ranked page, not the full set. 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, sector_erase_time, 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?
With no annotations provided, the description carries the full burden and does so thoroughly. It discloses the ranking algorithm (constraint satisfaction count, headroom, family spread), pagination behavior via matched vs returned, per-constraint pass/fail/unknown results, and case-sensitive canonical name requirements.
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 core prose is dense and well front-loaded, starting with purpose, then ranking behavior, then return semantics, then canonical list. The long canonical name list is somewhat redundant with the schema enum, but it is clearly organized and reinforces the case-sensitivity requirement.
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 complexity, the lack of annotations, and the presence of an output schema, the description is highly complete. It explains the API's surprising ranked-page behavior, the meaning of matched vs returned, and the exact constraint syntax and valid canonical values. Little is left for an agent to infer.
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 covers all parameters with descriptions, so the baseline is 3. The description adds important semantic context beyond the schema: constraints act as ranking/qualification criteria rather than simple filters, canonical names must be exact and case-sensitive, and unit is informational only for comparison.
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: 'Shortlist IC part numbers by parametric spec constraints.' It further defines the ranking logic and return semantics, which clearly differentiates it from siblings like search, lookup, compare_parts, and get_specs.
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 makes clear this tool is for parametric-constraint shortlisting, which is a distinct use case from keyword search or part comparison. However, it does not explicitly name alternative tools or state when not to use find_parts.
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_segments. Returns each image plus its caption/description/page
metadata. Only type='image' segments have image data; a ref that is not one comes
back as an error record whose available_images lists the document's figures nearest
that ref's page and section, closest first.
| Name | Required | Description | Default |
|---|---|---|---|
| refs | Yes | Opaque `ref` tokens (from search/lookup/get_segments) 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 provided, the description carries full responsibility for behavioral disclosure. It clearly explains return contents (image plus caption/description/page metadata), the type constraint (only type='image' segments), and the error behavior with available_images fallback. This is detailed and honest about edge cases.
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 three sentences with no filler. The core action and subject are front-loaded, followed by return details and edge-case behavior. Every sentence contributes directly to correct tool usage.
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 no output schema and no annotations, the description provides all essential context: input provenance, expected segment type, return metadata, and error handling. An agent can select and invoke this tool correctly without additional information.
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 refs is already documented. The description adds meaningful context beyond the schema: refs are opaque tokens from prior searches, image segments are required, and non-image tokens produce error records. This helps the agent construct valid inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: fetching datasheet figure images (PNG/JPEG) for opaque ref tokens. It also differentiates itself from siblings by specifying the token source (search/lookup/get_segments) and the segment type requirement, making its scope 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 gives clear context on when to use the tool: after obtaining ref tokens from search/lookup/get_segments, and specifically for image segments. It also explains the failure mode for non-image segments, but does not explicitly name an alternative tool for those cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_segmentsAInspectExpand 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 burden. It explains the core transformation (opaque refs expanded to full content) but doesn't disclose edge cases like invalid refs, rate limits, or whether the operation is read-only. The behavior is simple and implied, but a bit more detail would help.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that front-loads the action and resource, with zero filler. Every word earns its place.
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 an output schema, the description provides sufficient context to call it correctly. The relationship to search/lookup is clear, and the purpose is fully covered without needing extra explanation.
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. The description repeats what the refs parameter already says ('opaque ref tokens from search/lookup') without adding new semantic detail, keeping it at baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Expand') and identifies an exact resource ('opaque ref tokens') and output ('full datasheet segment content'). It clearly positions itself as the counterpart to search/lookup, distinguishing it from sibling tools that produce or compare 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 explicitly states that refs come from 'search/lookup', giving clear context that this tool is used after those tools. It doesn't explicitly state when not to use it, but the purpose is narrow enough that the implied usage is clear.
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 from a pre-extracted parametric index — not from datasheet text.
This is the cheapest and most precise source for numeric parameters where it has
coverage. Optionally restrict to specific canonical parameter names.
A part absent from results (with a "No specs found" warning) means no index
coverage, not that the datasheet lacks the parameter — fall back to lookup
for that part.
| 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, the description carries the full behavioral burden. It discloses the key semantic that an absent part means no index coverage, not that the datasheet lacks the parameter, and clarifies the data source's nature. It also exposes cost/quality characteristics ('cheapest and most precise') that are not visible from the schema.
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 concise, purposeful sentences: the first states the core function, the second gives usage guidance, and the third clarifies fallback semantics. Every sentence earns its place and the most important definition is 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?
Given an output schema exists and parameter schemas are fully described, the description covers all essential behavioral and usage context. It explains source, limitations, and fallback behavior, leaving no critical gap for an agent to invoke 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 description coverage is 100%, so the schema already fully documents both parameters. The description adds no additional per-parameter semantics beyond what the schema already states, so a baseline of 3 is appropriate here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Read'), a precise resource ('canonical extracted specs... from a pre-extracted parametric index'), and explicitly distinguishes itself from datasheet text. It clearly positions this tool relative to the sibling 'lookup' by naming it as the fallback, which removes ambiguity about what this tool does.
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?
It explicitly states when to prefer this tool ('cheapest and most precise source for numeric parameters where it has coverage') and gives an explicit fallback directive to 'lookup' when a part is absent from results. This provides actionable when-to-use/when-not-to-use guidance between siblings.
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_segments). 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?
No annotations are provided, so the description carries the full behavioral burden. It discloses that lookup is a retrieval operation, fuses search and get_segments, and groups results per part. However, it does not describe limits, pagination, error behavior, or what the full-content response looks like beyond the grouping detail.
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, front-loaded sentences with no wasted words. The core purpose appears immediately, and the usage guidance is integrated naturally rather than appended as filler.
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 only two parameters, one required, and an output schema present, the description is nearly complete for correct invocation. It covers the main use case, the part-scoping behavior, and the relationship to sibling tools; the only minor gap is the absence of explicit guidance about when to choose get_specs or compare_parts instead.
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. The description adds some context by explaining that parts scope and group retrieval, but that meaning is already largely present in the schema's parameter descriptions. No new syntax or format details are added beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: one-shot spec/section retrieval of the most relevant datasheet segments. It distinguishes the tool from siblings by explicitly naming search and get_segments as fused into this tool, so an agent can immediately tell what lookup does and how it differs.
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 selection guidance: 'prefer lookup over search when you know the part(s).' It also explains that results are grouped per part, which is a meaningful contrast with alternatives and helps an agent decide when lookup is the right choice.
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_segments for full content.
If you already know the part number(s), prefer lookup — it fuses this search
with get_segments in one call and groups full content per part. Use search
when the part is unknown, or to triage snippets before pulling 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?
With no annotations provided, the description carries the full burden. It clearly discloses that search only returns metadata and snippets, that refs are opaque, and that full content requires `get_segments`. It also explains scoping behavior and warns against cross-part contamination. This is strong behavioral context, though it doesn't cover edge cases like empty results or ranking behavior.
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 information-dense but every sentence earns its place: core behavior, return format, downstream usage, alternative tool routing, and scoping guidance. The most important facts are front-loaded, and there is no filler or repetition of schema 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?
Given that an output schema exists and the tool has several siblings, the description is complete enough for an agent to select and invoke it correctly. It covers what the tool does, what it returns, when to use it vs. `lookup`, how to pass refs onward, and how to scope by device. Nothing essential 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. The description adds useful semantics beyond the schema by giving a concrete scope syntax example (`scope='device:NE5532'`) and explaining how the opaque ref should be used with `get_segments`. This adds practical meaning without being redundant.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: 'Search the datasheet corpus' and explicitly describes the return type (hit records with metadata + snippet). It also distinguishes itself from the sibling `lookup` and `get_segments`, making its role clear without needing to inspect other schemas.
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 selection criteria: prefer `lookup` when part numbers are known, and use `search` when the part is unknown or when triaging snippets. It also provides concrete guidance for scoping with `scope='device:<MPN>'` and explains the downstream workflow with `get_segments`.
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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