bls-labor-mcp-server
Server Details
Fetch US Bureau of Labor Statistics data — CPI, unemployment, wages, JOLTS, and more via MCP.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- cyanheads/bls-labor-mcp-server
- GitHub Stars
- 1
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Tool Definition Quality
Average 4.7/5 across 6 of 6 tools scored.
Each tool has a clearly distinct purpose: list_surveys and search_series for discovery, get_series and get_latest for data retrieval (one for time-series, one for latest), and describe/query for analysis. No overlap.
All tools follow a consistent bls_ verb_noun pattern in snake_case (e.g., bls_get_series, bls_search_series), making naming predictable and clear.
With 6 tools, the set is well-scoped for BLS labor data: covers discovery, retrieval, and analysis without unnecessary bulk or missing essentials.
The tool surface supports the full workflow: discover surveys (list_surveys), search series (search_series), fetch data (get_series/get_latest), and analyze via SQL (describe/query). No obvious gaps.
Available Tools
6 toolsbls_dataframe_describeDescribe BLS DataframesARead-onlyIdempotentInspect
List canvas dataframes materialized by bls_get_series, with provenance (source tool, query parameters), TTL, row count, and column schema. Use before writing SQL to confirm column names. Lazy-sweeps expired tables before responding. Requires CANVAS_PROVIDER_TYPE=duckdb.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Optional table name (df_XXXXX_XXXXX) to describe a single dataframe. Omit to list all active dataframes. |
Output Schema
| Name | Required | Description |
|---|---|---|
| dataframes | Yes | Active dataframes for this tenant, newest first. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint and idempotentHint. Description adds behavioral details: 'Lazy-sweeps expired tables before responding' and environment requirement, providing context beyond 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?
Two concise sentences plus a requirement line. Every sentence adds value, front-loaded with purpose. 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 simple tool (1 optional param, output schema exists), the description covers purpose, usage hint, behavioral side-effect, and environment constraint. Complete for an agent to select and 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% and the description largely repeats the schema's parameter description. No additional semantic value beyond what the input schema already provides.
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 verb 'List' and resource 'canvas dataframes materialized by bls_get_series', clearly distinguishing from sibling tools like bls_dataframe_query (which queries) and bls_get_series (which retrieves series).
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 'Use before writing SQL to confirm column names' for context, and mentions prerequisite 'Requires CANVAS_PROVIDER_TYPE=duckdb'. Does not explicitly state when not to use, but sibling names imply alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
bls_dataframe_queryQuery BLS DataframesARead-onlyIdempotentInspect
Run a single-statement SELECT against the canvas dataframes registered by bls_get_series. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected. System catalogs (information_schema, pg_catalog, sqlite_master, duckdb_*) are denied at the bridge layer — use bls_dataframe_describe to list available dataframes. Supports JOINs, aggregates, window functions, and CTEs. Optional register_as persists the result as a new dataframe with a fresh TTL for chained analysis. Canvas SQL operations consume zero BLS API quota. Requires CANVAS_PROVIDER_TYPE=duckdb.
| Name | Required | Description | Default |
|---|---|---|---|
| sql | Yes | Single-statement SELECT against df_<id> tables on the shared canvas. Reference dataframes by the names returned in bls_get_series responses or listed by bls_dataframe_describe. Standard DuckDB SQL — joins, aggregates, window functions, CTEs all supported. Example: SELECT series_id, year, period, value FROM df_AAAAA_BBBBB WHERE year >= '2020' ORDER BY year DESC. | |
| preview | No | Inline row preview count. Defaults to row_limit. Set lower (e.g. 50) when chaining via register_as and only a sample is needed immediately. | |
| row_limit | No | Hard cap on rows materialized in the response (default 1000, max 10000). Full results live on-canvas under register_as when provided. | |
| register_as | No | When set, persist the query result as a new dataframe under this name. Fresh TTL — not inherited from parent tables. Use to chain analyses without re-running source SQL or consuming additional BLS quota. |
Output Schema
| Name | Required | Description |
|---|---|---|
| rows | Yes | Materialized rows, bounded by preview / row_limit. |
| notice | No | Guidance when results were capped by preview or row_limit — names which parameter was the binding limiter and suggests how to retrieve the rest. Absent when all rows fit in the response. |
| columns | Yes | Column names in projection order. |
| row_count | Yes | Total rows the query produced (may exceed rows.length when capped by row_limit). |
| expires_at | No | ISO 8601 expiry for the newly registered dataframe, when applicable. |
| registered_as | No | Set when register_as was supplied and the result was materialized. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds significant behavioral context: it is read-only, rejects certain SQL statements, denies system catalogs at the bridge layer, mentions zero BLS API quota consumption, and explains that register_as persists the result with a fresh TTL. No contradictions with 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?
The description is well-structured and front-loaded with the core purpose. Every sentence adds value: purpose, constraints, usage alternatives, supported operations, and optional parameter behavior. It is appropriately sized for a complex tool with 4 parameters and 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 tool complexity (4 parameters, required 1, output schema present), the description is complete. It covers purpose, constraints, usage guidelines, parameter semantics, and chaining. The output schema exists, so the description does not need to explain return values. No gaps identified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds substantial meaning beyond the schema: for 'sql' it provides an example and clarifies referencing dataframes; for 'preview' it explains default behavior and chaining; for 'row_limit' it explains hard cap and defaults; for 'register_as' it explains TTL and chaining benefits. This significantly helps the agent understand parameter usage.
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 it runs a single-statement SELECT against canvas dataframes registered by bls_get_series. It distinguishes itself from sibling tools like bls_dataframe_describe (for metadata) and bls_get_series (for fetching data). The verb 'Query' and resource 'BLS Dataframes' are specific and 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 explicitly states when to use this tool: for querying dataframes. It also specifies when not to: rejects writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions. It provides an alternative: use bls_dataframe_describe to list available dataframes. It also gives context on supported operations (JOINs, aggregates, etc.) and the optional register_as parameter for chaining.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
bls_get_latestGet Latest BLS ObservationARead-onlyIdempotentInspect
Return the single most recent observation for one or more BLS series. Use for "what is X right now" questions — the current unemployment rate, the latest CPI reading, etc. Each series consumes one API query against the 500/day limit; for the current value of many series, bls_get_series with a 1-year window is more quota-efficient (one query for up to 50 series). Recommended limit: 10 series; maximum: 50.
| Name | Required | Description | Default |
|---|---|---|---|
| series_ids | Yes | One or more BLS SeriesIDs (1–50). Each consumes one daily API query. Use bls_search_series to resolve concepts to SeriesIDs. Recommended: ≤10 series. |
Output Schema
| Name | Required | Description |
|---|---|---|
| failed | Yes | Series that failed to fetch. Inspect seriesId and error for per-item details. Not-found series appear here rather than as a tool-level error. |
| notice | No | Guidance when one or more series failed — e.g. to use bls_search_series to verify SeriesIDs. Absent when all series returned data. |
| results | Yes | Successfully fetched series with their latest observations. Series that failed appear in failed[] instead. |
| succeeded | Yes | Number of series with a successfully fetched observation. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint. Description adds API quota context (500/day limit, each series consumes one query) and notes alternative for efficiency. No contradiction. with annotations covering safety, description adds meaningful behavioral info beyond 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 sentences, each substantive. First sentence states primary function. Second gives usage guidance and alternatives. Third adds limits. 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 single parameter, annotations, output schema, and sibling tools, description covers purpose, usage context, quota concerns, and alternatives completely. No gaps.
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% (series_ids fully described). Description adds meaning beyond schema: explains per-series quota consumption and recommends ≤10 for efficiency. Provides context not in 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?
Clearly states 'return the single most recent observation for one or more BLS series'. Uses specific verb 'return' and resource 'BLS series'. Distinguishes from siblings like bls_get_series (returns range) and bls_search_series (resolves concepts).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says use for 'what is X right now' questions, provides alternatives (bls_get_series for quota efficiency), and gives recommended (≤10) and maximum (50) series limits.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
bls_get_seriesGet BLS Time-Series DataARead-onlyInspect
Fetch time-series data for 1–50 BLS series by SeriesID in a single API request (one query against the 500/day limit). Supports optional year range (up to 20 years per request) and BLS-computed period-over-period calculations (net change and percent change; a survey returns whichever it supports and silently omits the rest — CPI and PPI return percent change only, the inflation rate). Observations cover real periods only and are safe to sum or average as returned; set annual_average to add each year's annual-average row, which is that year's mean rather than an additional period. When the total observation count would exceed the inline context budget, results spill to a canvas dataframe and the response includes a dataset.name handle for follow-up SQL via bls_dataframe_query. Use bls_search_series first if you need to resolve a concept to a SeriesID.
| Name | Required | Description | Default |
|---|---|---|---|
| end_year | No | End year for the data range (inclusive). Defaults to the current year when omitted. | |
| series_ids | Yes | One or more BLS SeriesIDs (1–50). The entire batch counts as one API query. Use bls_search_series to resolve concepts to SeriesIDs. | |
| start_year | No | Start year for the data range (inclusive). The BLS API allows up to 20 years per request. Omit for the API default (typically 3–20 years depending on survey). | |
| calculations | No | When true, request BLS-computed period-over-period calculations. The flag is a single boolean (you cannot select an individual calculation type), but the API returns whichever the survey supports and omits the rest — CPI and PPI return percent change only (the inflation rate), and a survey that supports neither simply returns its observations without calculation fields. Requesting calculations never fails, so it is always safe to set; consult bls_list_surveys (allowsNetChange / allowsPercentChange) only to predict which fields will come back. Monthly-cadence series return each supported change type over 1, 3, 6, and 12-month intervals; other cadences return a subset. | |
| annual_average | No | When true, add each year's annual-average row to the observations. An annual average is the mean of that year's real periods, returned as an extra row named "Annual" with period M13 (monthly series), Q05 (quarterly) or S03 (semiannual) — not an additional month or quarter, so it must be excluded from any sum or average over observations. Defaults to false, which returns real periods only and is safe to aggregate directly. Independent of start_year/end_year. Surveys that publish no annual averages return the same rows either way; enrichment.annualAverageRows reports how many rows were actually added. |
Output Schema
| Name | Required | Description |
|---|---|---|
| notice | No | Guidance for agents — names any SeriesID that returned zero observations, and reports when results spilled to canvas and SQL is needed for full access. Absent when every requested series returned data and it all fit inline. |
| series | Yes | Series data, in request order. |
| dataset | No | Canvas dataframe handle — present when the observation volume exceeded the inline budget. Use bls_dataframe_query with dataset.name to run SQL across the full data. |
| spilled | Yes | True when results spilled to canvas due to inline budget overflow. |
| endYearApplied | No | End year in effect, when a range was requested. |
| seriesRequested | Yes | Number of SeriesIDs requested. Do not compare it against series[] length to find empty series — a SeriesID that returned no data is still listed in series[] with observationCount 0. Check observationCount per entry, or read notice, which names every SeriesID that came back empty. |
| startYearApplied | No | Start year in effect, when a range was requested. |
| annualAverageRows | No | How many observations across all series are annual-average rows (period M13/Q05/S03). Present only when annual_average is true; 0 means none of the requested surveys publish annual averages. |
| totalObservations | Yes | Total observation rows across all requested series. |
| calculationsApplied | No | Whether BLS net/percent-change calculations were requested. |
| annualAverageApplied | Yes | Whether annual-average rows were requested. When false, observations hold real periods only and can be summed or averaged directly. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant context beyond annotations: it details calculations behavior (silently omits unsupported types, safe to always set), annual_average adds a mean row with specific period codes, and observations spill to canvas when exceeding context. No contradictions with readOnlyHint or 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?
The description is detailed but well-structured, starting with core purpose then explaining each parameter and edge cases. Every sentence adds value, though a slight reduction in length would improve conciseness without losing information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity and the presence of an output schema, the description covers key behaviors, edge cases (calculations safe to set, annual_average mean row, spillover), and integration with sibling tools. It is sufficiently complete for an AI agent to use 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% but the description adds value: explains calculations behavior, annual_average row semantics and aggregation rules, and defaults for start_year/end_year. This helps the agent make informed decisions beyond basic parameter 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 it fetches time-series data for 1-50 BLS series by SeriesID in a single API request. It distinguishes from sibling tools like bls_search_series and bls_dataframe_query, providing specific verb+resource+constraints.
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 advises using bls_search_series first to resolve concepts and mentions that the batch counts as one query against the 500/day limit. It does not explicitly compare to bls_get_latest for single series or state when not to use this tool, but provides enough context for appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
bls_list_surveysList BLS SurveysARead-onlyIdempotentInspect
List BLS survey programs with their abbreviation codes, full names, and metadata about calculation support and annual averages. Use to discover which survey covers a topic before calling bls_search_series. Optional category filter narrows results to prices, employment, wages, productivity, injuries, or time_use surveys.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Optional category filter. One of: prices, employment, wages, productivity, injuries, time_use. Omit to list all surveys. |
Output Schema
| Name | Required | Description |
|---|---|---|
| total | Yes | Total surveys returned. |
| surveys | Yes | BLS survey programs matching the filter, sorted alphabetically by abbreviation. |
| categoryFilter | No | Category filter applied, if any. Absent when all surveys were listed. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent. Description adds what the tool returns (abbreviation codes, full names, metadata) and the optional category filter behavior, fully disclosing functionality.
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 efficient sentences with no wasted words. First sentence states purpose, second gives usage guidance and parameter function.
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 optional parameter, output schema, and clear annotations, the description fully covers the tool's purpose and usage context, including how it fits into a workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining the category filter narrows results to specific survey types, complementing the enum.
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 'List BLS survey programs' with specific details (abbreviation codes, full names, metadata). It distinguishes from sibling bls_search_series by advising use before that tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use to discover which survey covers a topic before calling bls_search_series.' Provides clear context for when to use, though no explicit 'when not' for other siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
bls_search_seriesSearch BLS SeriesARead-onlyInspect
Search the BLS series catalog by natural language query, survey code, geographic area, or keywords to resolve cryptic SeriesIDs. Returns matching series with decoded components (survey, area, item, seasonal flag) and plain-language names. Use this before bls_get_series when you have a concept but not a SeriesID. Operates offline — no API quota consumed. Survey filter accepts two-letter codes (CU, CE, LN, LA, PC, JT, OE, EC, PR). Area filter accepts state names, MSA names, or FIPS area codes.
| Name | Required | Description | Default |
|---|---|---|---|
| area | No | State name, MSA name, or FIPS area code to narrow results to a geographic area. Omit for national series. | |
| limit | No | Maximum number of results to return (1–50, default 10). | |
| query | Yes | Natural language or keyword query (e.g. "unemployment rate", "CPI food", "nonfarm payrolls"). Also accepts a SeriesID directly for exact lookup. | |
| survey | No | Two-letter LABSTAT survey abbreviation to filter results (e.g. CU for CPI, CE for CES, LN for CPS, LA for LAUS, JT for JOLTS, OE for OEWS). Omit to search all loaded surveys. | |
| seasonal_adjustment | No | When true, return only seasonally adjusted series. When false, return only not-seasonally-adjusted. Omit to return both. |
Output Schema
| Name | Required | Description |
|---|---|---|
| cap | No | The result limit that capped the returned list. |
| shown | No | Number of series returned in this response. |
| capped | Yes | True when the FTS candidate pool reached the internal cap (~1000). totalCount is then a lower bound, not an exact match count. Narrow the query, add survey/area filters, or use a direct SeriesID to get an exact count. |
| notice | No | Guidance when no results matched — e.g. how to broaden the query or remove filters. Absent when results are returned. |
| series | Yes | Matching series, ordered by relevance. |
| truncated | No | True when more candidates matched than the limit returned. |
| areaFilter | No | Area filter applied, if any. Absent when no area filter was passed. |
| totalCount | Yes | Total candidates scored before the limit was applied. A lower bound when capped is true — the catalog index may contain more matching series. |
| catalogSize | Yes | Total series in the loaded catalog index. Distinguishes an empty-result search from a failed catalog load. |
| limitApplied | Yes | Result limit in effect (defaults to 10 when omitted). |
| surveyFilter | No | Survey filter applied, if any. Absent when no survey filter was passed. |
| effectiveQuery | Yes | Query string as the server received and searched on. Confirms interpretation for self-correction. |
| seasonalFilter | No | Seasonal-adjustment filter applied, if any. Absent when not passed. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds valuable context beyond this: it operates offline, consumes no API quota, and returns decoded components and plain-language names. No contradictions with 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?
The description is concise with no wasted words. It front-loads the primary purpose, then adds supporting details in separate sentences. Every sentence serves a purpose and contributes to clarity.
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 (5 parameters, output schema present), the description covers all essential aspects: search type, parameter formats, return format, and usage context. The presence of an output schema reduces the need to describe return values in detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds extra meaning by listing more survey codes, clarifying that query accepts SeriesID for exact lookup, and explaining area filter options. This enrichment justifies a score 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?
The description clearly states the tool's verb ('Search'), resource ('BLS series catalog'), and how it interacts with siblings. It explicitly mentions resolving cryptic SeriesIDs and distinguishes itself by advising use before bls_get_series when lacking a SeriesID.
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 when to use the tool ('Use this before bls_get_series when you have a concept but not a SeriesID') and provides alternative tools (bls_get_series). It also notes offline operation and no API quota, which informs decision-making.
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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