get_state_market_briefEvery public number about marketing a law firm in one state or DC, joined from seven open datasets. Each figure carries its source and check date. Not a forecast. Results are not legal advice.
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | Two-letter code (TX) or DC; full names (Texas) also work | |
| response_format | No | concise |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Input schema / properties / detailRemoved value: -{
- "default": "summary",
- "enum": [
- "summary",
- "full"
- ],
- "type": "string"
-}Input schema / properties / response_formatAdded value: +{
+ "default": "concise",
+ "enum": [
+ "concise",
+ "detailed"
+ ],
+ "type": "string"
+}Input schema / properties / detail / descriptionRemoved value: -"summary (default): each record as its id, a 1-2 sentence text, status, date checked, one source URL and the page to cite. full: every field, every source and the per-record provenance."Output schema / propertiesRemoved value: -{
- "meta": {
- "description": "Credit line (attribution), citation, DOI, version, license (CC BY 4.0), disclaimer, notice and the page to cite",
- "required": [
- "attribution",
- "license",
- "disclaimer",
- "notice"
- ],
- "type": "object"
- },
- "nextCursor": {
- "description": "Pass as `cursor` to get the next page; null on the last page",
- "type": [
- "string",
- "null"
- ]
- },
- "provenance": {
- "description": "Where this came from: the publisher of the dataset, the dataset and its version DOI, the record id, the original source (who published it and its URL), the day that source was last checked, and when this answer was retrieved",
- "properties": {
- "citeUrl": {
- "description": "The page to cite",
- "type": [
- "string",
- "null"
- ]
- },
- "dataset": {
- "description": "Dataset id",
- "type": "string"
- },
- "datasetTitle": {
- "type": "string"
- },
- "dateChecked": {
- "description": "YYYY-MM-DD the record was last checked against its source",
- "type": [
- "string",
- "null"
- ]
- },
- "doi": {
- "description": "Version DOI",
- "type": [
- "string",
- "null"
- ]
- },
- "doiUrl": {
- "type": [
- "string",
- "null"
- ]
- },
- "line": {
- "description": "The same facts as one sentence, to quote under an answer",
- "type": "string"
- },
- "publisher": {
- "description": "Publisher of the dataset",
- "type": "string"
- },
- "recordId": {
- "description": "Stable id of the record",
- "type": [
- "string",
- "null"
- ]
- },
- "retrievedAt": {
- "description": "ISO 8601 time this answer was served",
- "type": "string"
- },
- "source": {
- "description": "The original source: statute, rule, bar opinion, court report, agency table or platform policy",
- "type": [
- "string",
- "null"
- ]
- },
- "sourceKind": {
- "description": "primary or secondary",
- "type": [
- "string",
- "null"
- ]
- },
- "sourceUrl": {
- "type": [
- "string",
- "null"
- ]
- },
- "status": {
- "description": "P primary source, S secondary, D default (no specific rule found), U open question",
- "type": [
- "string",
- "null"
- ]
- },
- "version": {
- "description": "Dataset version",
- "type": "string"
- }
- },
- "required": [
- "publisher",
- "dataset",
- "version",
- "retrievedAt"
- ],
- "type": "object"
- }
-}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, closed-world, and non-destructive, so the safety profile is covered. The description adds real behavioral value beyond that: results are joined from seven open datasets and each figure is provenance-tagged with a source and check date, plus explicit caveats that no forecasting or legal interpretation is included.
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 compact sentences with the core payload front-loaded in the first clause, followed by provenance and caveats. Every sentence carries information; the two negative disclaimers are short and arguably necessary for an agent answering legal-adjacent questions.
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 an output schema present, return structure needn't be described, and the description adequately covers data origin, provenance, and scope limits. Minor gaps remain around data freshness beyond the per-figure check date and the practical difference between the concise and detailed response formats.
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 50% — the state parameter is well documented in the schema itself (two-letter code, DC, or full name), while response_format has only an enum and default with no prose. The description contributes nothing about either parameter, so it neither compensates for the gap nor adds meaning; baseline 3 applies.
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 names a specific resource — an aggregated market brief of public marketing numbers for a single state or DC, drawn from seven open datasets — which is more concrete than a bare title. It implicitly contrasts with multi-state siblings like compare_states by scoping to 'one state or DC', though it never names those siblings explicitly.
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?
Scope is implied ('one state or DC') and the disclaimers 'Not a forecast' and 'Results are not legal advice' set interpretive boundaries. However, there is no explicit when-to-use guidance, no mention of when to prefer compare_states or get_state_rules, and no stated prerequisites.
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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