The Texas Voice strives to be an honest and trustworthy source of information about Texas politics and government.
There is one clear pair of overlapping tools: `search` and `search_articles` both find content, and `fetch`/`get_article` both retrieve full text, though by different identifiers. The descriptions help, but an agent could easily choose the wrong workflow when asked to retrieve an article.
Most names are lowercase snake_case verbs or verb phrases (`get_article`, `list_recent_articles`, `search_articles`), but `popular_content` is a noun phrase and `fetch`/`search` are bare verbs. This is readable but not a tight, predictable convention.
Six tools is a good size for a read-only publication API. Each tool has a reasonable role, and the set avoids bloat while still offering discovery, search, recency, popularity, and full-text retrieval.
The domain is content access for a publication, and the surface covers discovery (list, popular, search), article metadata, and full-text retrieval by both id and slug. There are no dead ends: any search result can lead to full text, and recent/popular lists support browsing.
Retrieve the full text of one document by the id returned from search.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Document id from search results |
| Name | Required | Description |
|---|---|---|
| id | Yes | |
| url | Yes | |
| text | Yes | |
| title | Yes | |
| metadata | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the description does not need to restate safety. It adds useful context by specifying that the tool returns the full text of one document rather than metadata or a list. There is no contradiction 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 a single sentence, efficient and front-loaded with the action ('Retrieve the full text') and resource ('one document'), followed by the key input source. Every word adds value; there is no wasted text.
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 read-only tool with an output schema and full annotation coverage, the description is complete. It says what is fetched, how many documents, and where the id comes from. Nothing essential is missing 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?
The schema already provides 100% coverage for the single parameter with the description 'Document id from search results'. The tool description reinforces this by saying 'id returned from search', but adds no new semantic detail 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 clearly states a specific verb and resource: 'Retrieve the full text of one document' by id. It differentiates from search/list tools by specifying a single document and full text, but it does not explicitly distinguish itself from the sibling 'get_article', which may serve a similar purpose.
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 gives clear usage context: use this tool with an id that comes from `search`. This implies the intended flow of search-then-fetch. However, it does not mention when to prefer this over sibling tools like `get_article`, nor does it state exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch the full clean text of one article by its slug (the last path segment of an article URL).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Article slug, e.g. "my-article-title" |
| Name | Required | Description |
|---|---|---|
| url | Yes | |
| date | No | |
| slug | Yes | |
| text | No | |
| title | Yes | |
| summary | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is established. The description adds the useful behavior that it returns 'full clean text' and clarifies slug extraction from the URL, but does not go further into response details or edge cases; that is acceptable given the output 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?
The description is a single sentence with no filler. It front-loads the core purpose and includes the essential slug clarification in a parenthetical, making it easy to parse and remember.
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?
This is a simple one-parameter read-only tool with a full output schema and detailed annotations. The description plus schema provide everything an agent needs to call it correctly, including how to obtain the slug from an article URL.
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 documents 'slug' with an example, so baseline is 3. The description adds extra meaning by defining a slug as 'the last path segment of an article URL,' which helps the agent construct the correct parameter value from a URL.
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 ('fetch'), a specific resource ('one article'), and the identifying mechanism ('by its slug'). It clearly differentiates from sibling tools like list_recent_articles or search_articles, which operate over collections or use queries.
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 the usage context clear: use this tool when you want the full text of a specific article and have its slug. It does not explicitly mention alternatives or exclusions, but for such a focused single-resource tool the context is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the most recently published articles from this publication.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 5, max 20) |
| Name | Required | Description |
|---|---|---|
| total | No | |
| articles | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is well covered. The description adds useful behavioral context by specifying recency and publication scope, but it does not disclose additional traits such as ordering details, time windows, or pagination 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 a single, front-loaded sentence that states exactly what the tool does without any filler or redundancy. Every word contributes to the meaning.
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 read-only list tool with one fully documented optional parameter and an output schema, the description is complete. An agent has enough information to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the limit parameter is already fully documented with its default and maximum values. The description does not need to add parameter detail, and it does not, so the baseline of 3 is appropriate.
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: 'List the most recently published articles from this publication.' This clearly distinguishes it from siblings like popular_content and search_articles, which target popularity or search queries rather than chronological recency.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use the tool—when a listing of recently published articles is needed. However, it does not explicitly state when not to use it or name alternatives such as popular_content or search_articles, so the usage 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.
The most-read / most-requested articles, ranked by how often AI assistants have actually fetched them. Use for "what is your most popular article", "top reads", or to recommend what to read.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Look-back window in days (default 30, max 365) | |
| limit | No | How many to return (default 10, max 25) |
| Name | Required | Description |
|---|---|---|
| articles | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context by explaining the ranking is based on actual AI assistant fetches, which is non-obvious and useful. 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?
Two sentences, no wasted words. The core meaning is front-loaded in the first sentence, and the second gives direct usage examples. Efficient and readable.
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 read-only tool with only two optional parameters, an output schema, and rich annotations, the description fully covers what the tool does, why it exists, and when to invoke it. Nothing critical 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 description coverage is 100%, and both parameters (days, limit) have clear schema descriptions. The tool description doesn't add extra parameter meaning, but it doesn't need to; the schema carries the burden.
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 defines the resource (popular articles) and the distinguishing criterion (ranked by how often AI assistants have fetched them). This differentiates it from siblings like list_recent_articles or search, even without naming them, because the popularity angle is explicit.
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 provides concrete example queries ('what is your most popular article', 'top reads') and the recommend-what-to-read use case. It doesn't explicitly mention when not to use it or name alternatives, but the context is unambiguous for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search this site's content. Returns matching documents (id, title, url); pass an id to fetch for the full text.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query |
| Name | Required | Description |
|---|---|---|
| results | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the safety profile (read-only, idempotent, non-destructive). The description adds useful behavioral context by stating that results contain only metadata (id, title, url) and that full text requires a follow-up fetch call.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler; the action and result are front-loaded, and the fetch follow-up 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 one-parameter, read-only search tool with an output schema and safety annotations, the description provides everything essential: purpose, searched resource, return fields, and the next step for full text. Nothing important 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 description coverage is 100% and the single query parameter is already described as a search query. The tool description does not add syntax, formatting, or example guidance, so the baseline of 3 is appropriate.
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 identifies a search action over site content and lists the returned document fields (id, title, url), so an agent knows what the tool does. However, it does not explicitly distinguish itself from the sibling tool search_articles, which appears to overlap in purpose, so it stops short of full sibling differentiation.
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 the usage context clear: use it to find matching documents and then pass the returned id to fetch for full text. It does not, however, state when to prefer this tool over search_articles or other sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search this publication's articles by keyword. Returns the best-matching titles, dates, canonical URLs, and short summaries.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 5, max 20) | |
| query | Yes | Search keywords |
| Name | Required | Description |
|---|---|---|
| total | No | |
| articles | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context by noting that results are 'best-matching' and describing the return fields, which helps the agent understand result ranking and content without contradicting the 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 a single sentence that front-loads the core action and resource, then lists the return fields efficiently. Every clause adds useful information with no redundancy or 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?
For a simple two-parameter read-only tool with a present output schema and strong annotations, the description is largely complete. The only missing context is explicit sibling differentiation guidance for cases where the agent might consider 'search' or 'fetch', but this is minor given the clear scoping to publication articles.
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%, with both query and limit already well documented in the schema. The description's 'by keyword' phrase adds little beyond the schema's 'Search keywords', so no significant new parameter meaning is provided.
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 verb ('Search'), a specific resource ('this publication's articles'), and the input type ('by keyword'). It also states what is returned (titles, dates, canonical URLs, short summaries), which clearly distinguishes it from siblings like list_recent_articles and popular_content.
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 the tool is for keyword-based article search, but it does not explicitly state when to use this tool over the sibling 'search' or other alternatives like list_recent_articles. The context is clear enough for basic routing but lacks explicit exclusions or alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
fetch1 field changedOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "id": {
+ "type": "string"
+ },
+ "metadata": {
+ "type": "object"
+ },
+ "text": {
+ "type": "string"
+ },
+ "title": {
+ "type": "string"
+ },
+ "url": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "id",
+ "title",
+ "text",
+ "url"
+ ],
+ "type": "object"
+}get_article1 field changedOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "date": {
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "slug": {
+ "type": "string"
+ },
+ "summary": {
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "text": {
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "title": {
+ "type": "string"
+ },
+ "url": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "slug",
+ "title",
+ "url"
+ ],
+ "type": "object"
+}list_recent_articles1 field changedOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "articles": {
+ "items": {
+ "properties": {
+ "date": {
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "slug": {
+ "type": "string"
+ },
+ "summary": {
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "title": {
+ "type": "string"
+ },
+ "url": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "slug",
+ "title",
+ "url"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "total": {
+ "type": [
+ "integer",
+ "null"
+ ]
+ }
+ },
+ "required": [
+ "articles"
+ ],
+ "type": "object"
+}popular_content1 field changedOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "articles": {
+ "items": {
+ "properties": {
+ "requests": {
+ "type": "integer"
+ },
+ "title": {
+ "type": "string"
+ },
+ "url": {
+ "type": [
+ "string",
+ "null"
+ ]
+ }
+ },
+ "required": [
+ "title"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "articles"
+ ],
+ "type": "object"
+}search1 field changedOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "results": {
+ "items": {
+ "properties": {
+ "id": {
+ "type": "string"
+ },
+ "title": {
+ "type": "string"
+ },
+ "url": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "id",
+ "title",
+ "url"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "results"
+ ],
+ "type": "object"
+}search_articles1 field changedOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "articles": {
+ "items": {
+ "properties": {
+ "date": {
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "slug": {
+ "type": "string"
+ },
+ "summary": {
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "title": {
+ "type": "string"
+ },
+ "url": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "slug",
+ "title",
+ "url"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "total": {
+ "type": [
+ "integer",
+ "null"
+ ]
+ }
+ },
+ "required": [
+ "articles"
+ ],
+ "type": "object"
+}popular_contentfetchget_articlelist_recent_articlessearchsearch_articlesClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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