The digikey-mcp server is a read-only DigiKey electronics sourcing assistant that integrates the DigiKey Product Information V4 API to help engineers search, evaluate, and plan Bills of Materials (BOMs).
Search Parts: Query DigiKey's catalog using natural language (e.g., "24V to 5V 5A buck converter") to get an evidence-backed shortlist of candidates with trade-off analysis.
Get Part Details: Fetch authoritative, current pricing, availability, specifications, datasheet links, and product URLs for a specific DigiKey Part Number (DKPN) or Manufacturer Part Number (MPN).
Build a Full Electronics BOM: Plan a block-level architecture from a project description, source and verify components, calculate quantities (including spares), and save a versioned bom.json artifact.
Refine a BOM: Apply new requirements (e.g., "must use 0603 package") to an existing BOM, preserving verified lines, explaining invalidations, and re-running validation.
Validate a BOM: Check evidence freshness, identifiers, duplicate lines, quantities, and engineering consistency, returning machine-readable issues and a structured summary.
Export an Order List: Export verified BOM lines as TSV, CSV, JSON, or Markdown. POSSIBLE_MATCH lines require explicit opt-in and are flagged as unsafe for ordering.
Compare Candidates: Compare multiple DKPNs side-by-side against explicit requirements, highlighting verified facts and trade-offs without declaring a subjective winner.
Key constraints:
Operates in offline (default, synthetic data), sandbox, or production modes.
Does not place orders, check out, or modify DigiKey accounts.
Does not invent part numbers, prices, or compatibility claims.
Human review is required for safety-critical decisions.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@digikey-mcp search for 24V to 5V 5A buck converter"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
digikey-mcp is a read-only DigiKey Product Information V4 sourcing assistant for electronics
engineers. It provides candidate search, authoritative part details, requirements-led BOM
planning, refinement, validation, comparison, and fail-closed order-list export through a CLI and
a genuine MCP stdio server.
For AI agents: Read
AGENT.mdbefore installing, importing, configuring, or using this repository. It explains the MCP server, the companion skill, supported workflows, safety rules, and verification steps.
It does not place orders, change DigiKey accounts, check out, scrape the DigiKey website, or invent part numbers, prices, stock, specifications, URLs, or compatibility claims. Human review is required for critical electrical, thermal, EMC, and safety decisions.
Python 3.11+ is required. uv is recommended for the reproducible clone-and-run workflow:
uv sync --all-groups
uv run python -m digikey_bom --helpThe project exposes digikey-bom and digikey-mcp console scripts after installation.
uv is not required at runtime. To use the standard Python tooling instead:
python -m venv .venv
# macOS/Linux
source .venv/bin/activate
# Windows PowerShell: .venv\\Scripts\\Activate.ps1
python -m pip install --upgrade pip
python -m pip install .
digikey-mcp --helpFor a published package, run python -m pip install digikey-mcp once the package is available
from your chosen package index. Offline mode works without DigiKey credentials or network access.
Related MCP server: jlcpcb-mcp
Copy .env.example to .env only for local configuration. Supported modes are:
offline (default): synthetic, visibly fixture-labeled data; no credentials or network.
sandbox: DigiKey sandbox Product Information V4 API.
production: DigiKey production Product Information V4 API.
Sandbox and production require a DigiKey developer subscription/application and the process
environment variables DIGIKEY_CLIENT_ID, DIGIKEY_CLIENT_SECRET, and DIGIKEY_ACCOUNT_ID for
ProductDetails calls. OAuth uses the two-legged client_credentials flow; tokens are cached in
memory only and never written to artifacts or logs. The client uses only official V4 keyword-search
and ProductDetails endpoints.
Keyword results are discovery evidence and may be stale. Current price and availability for a purchase-ready line come from ProductDetails. Rate limits and transient failures use bounded, injectable retries.
Offline examples work without credentials:
python -m digikey_bom search "24V to 5V 5A buck converter" --mode offline
python -m digikey_bom details DEMO-10K-0603-ND --mode offline
python -m digikey_bom bom "Build a Raspberry Pi 5 robot power and I/O BOM" --build-quantity 2 --format markdown --output-dir output
python -m digikey_bom validate-bom output/bom.json --mode offline
python -m digikey_bom export-bom output/bom.json --format tsv --mode offline
python -m digikey_bom refine-bom output/bom.json "must use 0603 package" --mode offline
python -m digikey_bom compare DEMO-10K-0603-ND DEMO-10K-REEL-ND --mode offline
digikey-mcp serveUse --json on search, details, BOM, refine, validate, and compare for complete structured data.
export-bom emits the selected format to stdout. The default export excludes POSSIBLE_MATCH and
NO_VERIFIED_PART_FOUND; --include-possible-matches is explicitly unsafe review output.
The server exposes exactly these tools over stdio:
digikey_search_parts, digikey_get_part_details, digikey_build_electronics_bom,
digikey_refine_bom, digikey_validate_bom, digikey_export_order_list, and
digikey_compare_candidates.
Example Claude Desktop/Claude Code-style configuration:
{
"mcpServers": {
"digikey": {
"command": "uv",
"args": ["run", "--project", "/absolute/path/to/digikey-mcp", "digikey-mcp", "serve"],
"env": {"DIGIKEY_MODE": "offline"}
}
}
}For live use, set DigiKey variables in the client environment. Do not paste secrets into prompts or skill instructions.
Every BOM line has one status:
VERIFIED_MATCH: exact DKPN observed in ProductDetails and every critical requirement verified.
VERIFIED_WITH_ASSUMPTION: exact DKPN observed; remaining engineering assumptions are explicit.
POSSIBLE_MATCH: exact DKPN observed, but a critical fact is missing, stale, or ambiguous.
NO_VERIFIED_PART_FOUND: no safe part selected; failure and next action are retained.
The canonical artifact is schema 1.0.0, saved as output/bom.json by default. Optional views
include requested/purchase quantity, DKPN, MPN, package/MOQ, stock, price, lifecycle, datasheet,
product URL, and selection notes. Custom, user-provided, and non-DigiKey items remain separate.
The portable Claude-compatible skill is skills/digikey-bom/SKILL.md,
with workflow references and examples for robot power, CAN, sensor boards, multi-board BOMs, and
substitutions.
Agent handoff instructions are in AGENT.md.
Default tests are offline and use synthetic or mocked official-API-shaped responses. No real credentials or network calls are required:
uv run ruff format --check .
uv run ruff check src tests
uv run mypy src
uv run pytest -q
uv build
uv run twine check dist/*See CONTRIBUTING.md and SECURITY.md.
DigiKey price, stock, lifecycle, and availability data change. A returned part is not a complete electrical design, certification, thermal proof, safety case, EMC result, or schematic/layout review. Re-check ProductDetails before purchasing and obtain qualified human review for power, fault, battery, motor, E-stop, and other safety-critical decisions.
Copyright 2026 digikey-mcp contributors. Licensed under Apache-2.0; see LICENSE.
Plan a block-level electronics architecture, build a requirements ledger, source eligible lines, validate quantities/evidence, and save a versioned bom.json. Custom, user-provided, and non-DigiKey items remain separate and unresolved.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | offline | |
| lines | No | ||
| budget | No | ||
| currency | No | ||
| output_dir | No | ||
| constraints | No | ||
| requirements | No | ||
| target_region | No | ||
| build_quantity | No | ||
| lifecycle_policy | No | ||
| preferred_package | No | ||
| project_description | Yes | ||
| approved_manufacturers | No |
| Name | Required | Description |
|---|---|---|
| data | No | |
| errors | No | |
| success | Yes | |
| summary | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It mentions saving a versioned bom.json and keeping custom items separate, but does not explain side effects like network access, auth requirements, or the effect of modes (offline/sandbox/production) from the schema. The description lacks detail on what happens when items are unresolved.
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 efficiently lists the tool's actions. It is not verbose, but could be more structured with bullet points or separate lines to improve readability. No unnecessary 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?
Given the complexity (13 parameters, nested objects) and the presence of an output schema, the description is insufficient. It does not specify how inputs like constraints, budget, or preferred_package are used. The output is mentioned briefly, but the overall behavior in relation to inputs is unclear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the schema provides no parameter descriptions. The tool's description does not explain any of the 13 parameters, not even the required 'project_description'. This leaves the agent guessing about input semantics.
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 purpose: planning architecture, building a requirement ledger, sourcing lines, and saving a BOM. However, it does not explicitly distinguish itself from sibling tools like digikey_refine_bom or digikey_validate_bom, which might perform similar tasks.
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 guidance on when to use this tool versus alternatives. The description implies it is for building a BOM from scratch, but there is no mention of when not to use it or which sibling would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare candidate DKPNs against explicit requirements and show verified facts and trade-offs without claiming a user-dependent objective winner.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | offline | |
| candidates | No | ||
| identifiers | No | ||
| requirements | No |
| Name | Required | Description |
|---|---|---|
| data | No | |
| errors | No | |
| success | Yes | |
| summary | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral transparency burden. It states the tool 'show verified facts and trade-offs without claiming a user-dependent objective winner,' which is a useful behavioral constraint. However, it lacks details on whether the tool is read-only, requires authentication, or has side effects.
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, concise sentence that is front-loaded with the core purpose. No extraneous words 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 complexity of the input schema (nested objects like Candidate, Evidence, Requirement) and the absence of parameter descriptions, the tool is inadequately specified for an AI agent to construct valid arguments. Output schema exists but does not compensate for missing input guidance.
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?
Input schema has 4 parameters (mode, candidates, identifiers, requirements) with 0% schema description coverage. The description provides no explanation of these parameters, their structure (e.g., how to specify candidates or requirements), or their relationship to each other. The agent cannot determine how to populate the arguments.
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 action ('Compare candidate DKPNs against explicit requirements') and its output ('show verified facts and trade-offs'). It distinguishes itself from sibling tools (e.g., search, get details) by focusing on comparison. However, it does not explicitly differentiate from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., digikey_search_parts, digikey_get_part_details). There are no prerequisites, exclusions, or context indicating the appropriate scenario for using this comparison tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Export only VERIFIED_MATCH and VERIFIED_WITH_ASSUMPTION lines by default as TSV, CSV, JSON, or Markdown. POSSIBLE_MATCH requires explicit opt-in and remains unsafe.
| Name | Required | Description | Default |
|---|---|---|---|
| bom | No | ||
| mode | No | offline | |
| format | No | tsv | |
| output_dir | No | ||
| saved_path | No | ||
| include_possible_matches | No |
| Name | Required | Description |
|---|---|---|
| data | No | |
| errors | No | |
| success | Yes | |
| summary | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It mentions default filtering and that POSSIBLE_MATCH is 'unsafe', but lacks detail on side effects, authentication needs, rate limits, or what happens to the data. The source of the lines is also unclear.
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 concise at two sentences, front-loading the key behavior and format options. It is easy to parse, though could benefit from a brief note on the bom parameter.
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 of the tool (6 parameters) and missing annotations, the description is insufficient. It does not explain the primary input (bom), output locations, or behavior of mode. The output schema exists but the description still lacks context for many parameters.
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 partially compensates by explaining the format parameter (TSV, CSV, JSON, Markdown) and the include_possible_matches parameter ('POSSIBLE_MATCH requires explicit opt-in'). However, it leaves the bom, mode, output_dir, and saved_path parameters undefined.
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 specifies the action ('Export'), the resource ('only VERIFIED_MATCH and VERIFIED_WITH_ASSUMPTION lines'), and the output formats (TSV, CSV, JSON, Markdown). It clearly distinguishes from sibling tools by focusing on exporting an order list.
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 when to use (for exporting certain verified lines) and provides a condition ('POSSIBLE_MATCH requires explicit opt-in'), but does not explicitly contrast with sibling tools or give guidance on when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch authoritative DigiKey ProductDetails for a DKPN or MPN, including current pricing/availability, links, normalized parameters, and evidence metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| quantity | No | ||
| identifier | Yes | ||
| requirements | No |
| Name | Required | Description |
|---|---|---|
| data | No | |
| errors | No | |
| success | Yes | |
| summary | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. 'Fetch' implies read-only behavior, and listed contents give some idea of expected output. However, missing details on authentication, rate limits, or error handling for invalid identifiers.
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?
Description is a single well-structured sentence that front-loads the tool's purpose and key outputs. However, it could be slightly expanded to cover parameter details without losing conciseness.
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?
Although the output schema may cover return values, the description is inadequate for the tool's complexity (3 parameters, one nested). It lacks parameter guidance and usage context, leaving gaps for the AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and description does not explain any of the three parameters (identifier, quantity, requirements). It does not state that identifier expects a DKPN or MPN, nor does it describe quantity or the complex requirements object.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool fetches authoritative DigiKey product details for a DKPN or MPN, listing included data (pricing, availability, links, normalized parameters, evidence metadata). This distinguishes it from sibling tools like search or comparison 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?
Description implies usage context (when you need detailed product info for a specific identifier) but does not explicitly state when not to use or mention alternatives. Sibling tools are listed but not compared.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Apply a new explicit requirement to an existing structured BOM, preserve unrelated verified lines, explain invalidations/replacements, and rerun validation.
| Name | Required | Description | Default |
|---|---|---|---|
| bom | No | ||
| mode | No | offline | |
| output_dir | No | ||
| saved_path | No | ||
| requirement | Yes | ||
| affected_line_ids | No |
| Name | Required | Description |
|---|---|---|
| data | No | |
| errors | No | |
| success | Yes | |
| summary | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description provides behavioral details such as preserving unrelated lines and rerunning validation, but it omits side effects like auth requirements or performance constraints.
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 efficiently covers key actions with 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?
Given the output schema exists, the description sufficiently covers the tool's process, though it could mention that the output is an updated BOM.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description does not explain individual parameters beyond the requirement. It fails to compensate for the missing schema descriptions for bom, mode, output_dir, etc.
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 applies a new requirement to a structured BOM, preserves unrelated verified lines, explains invalidations/replacements, and reruns validation. This distinguishes it from siblings like digikey_build_electronics_bom and digikey_validate_bom.
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 needing to add a requirement to an existing BOM, but it lacks explicit guidance on when to prefer this tool over alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search DigiKey candidates using a natural-language query, enrich the shortlist with authoritative ProductDetails, and preserve evidence-backed trade-offs. Offline results are synthetic and never purchase-ready.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | offline | |
| limit | No | ||
| query | Yes | ||
| quantity | No | ||
| page_count | No | ||
| requirements | No | ||
| filter_options | No | ||
| shortlist_size | No |
| Name | Required | Description |
|---|---|---|
| data | No | |
| errors | No | |
| success | Yes | |
| summary | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry burden. It discloses that offline results are synthetic and not purchase-ready, but doesn't mention authentication, rate limits, or mode-specific behaviors.
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 concise with two sentences, no unnecessary words. However, it lacks structured formatting like bullet points for key aspects.
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 8 parameters with no schema descriptions and no output schema in context, the description is incomplete. It doesn't explain return values, pagination, or how to interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description adds no parameter-level detail. It mentions 'natural-language query' but doesn't explain parameters like mode, limit, or requirements.
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 purpose: search DigiKey candidates using natural language, enrich with ProductDetails, and preserve trade-offs. It distinguishes from sibling tools like building BOMs or comparing candidates.
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 lacks explicit when-to-use guidance or alternatives. It implies usage for initial part exploration but doesn't contrast with siblings like get_part_details or validate_bom.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate BOM evidence, identifiers, freshness, duplicate lines, quantities, and engineering/interface consistency; return machine-readable issues and a summary.
| Name | Required | Description | Default |
|---|---|---|---|
| bom | No | ||
| mode | No | offline | |
| output_dir | No | ||
| saved_path | No |
| Name | Required | Description |
|---|---|---|
| data | No | |
| errors | No | |
| success | Yes | |
| summary | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. It states the tool 'validates' and 'returns' results, implying a read-only operation, but does not clarify side effects (e.g., file saving via output_dir/saved_path) or whether it modifies state. The absence of such details reduces transparency.
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 containing multiple validation aspects, which is concise but lacks structure. It could be split into clear sections (e.g., 'Validates... Returns...') to improve readability without adding length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output schema covers return values, but the description fails to explain parameters, usage context, or behavioral traits. For a tool with 4 parameters and no annotations, this is insufficient for effective agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no information about parameters (bom, mode, output_dir, saved_path). All four parameters are undocumented, forcing the agent to infer their meaning from names alone. The description adds zero value 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 uses specific verbs and resource: 'Validate BOM evidence, identifiers, freshness, duplicate lines, quantities, and engineering/interface consistency'. This clearly distinguishes it from sibling tools like digikey_build_electronics_bom (creation), digikey_compare_candidates (comparison), digikey_refine_bom (refinement), etc.
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 provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, context, or situations where another tool would be more appropriate. Users must infer usage from the name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose: building BOMs, comparing candidates, exporting, fetching part details, refining BOMs, searching, and validating. No overlap or ambiguity.
All tools follow a consistent `digikey_verb_noun` pattern with underscores, making the function of each tool immediately recognizable.
7 tools is well-scoped for a BOM management and parts search server, covering key workflows without being excessive or minimal.
The tool set covers search, detail retrieval, BOM building, refining, comparing, validating, and exporting. Minor gap: no explicit delete or update of individual BOM lines, but refining handles changes.
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