Read-only access to Compensation Professional's compensation-focused book library, query-shaped...
Each tool targets a clearly distinct entity: authored answers, library works, wage jurisdictions, and feed metadata. The only adjacent pair, search_library and get_library_work, is naturally complementary with discovery vs. retrieval.
Most tools follow a get_<entity> snake_case pattern, which is clean and predictable. The exceptions, list_compensationprofessional_feeds and search_library, are somewhat inconsistent but remain readable and unlikely to confuse.
Five tools is a well-scoped set for a niche reference server covering answers, library records, and wage data. Each tool has a clear purpose and there is no redundancy.
Core retrieval works for known entities, but discovery is incomplete: answers and wage jurisdictions can only be fetched when the slug or jurisdiction is already known. The library has search support, but the answer and wage-compliance pillars lack list or search equivalents.
Returns one authored /answers page as structured content: question, short answer, problem frame, and citations. Known slugs (13): how-to-set-pay-ranges, what-should-we-pay-for-a-role, is-our-pay-competitive, how-to-tie-executive-pay-to-performance, executive-compensation-components, how-much-executive-pay-at-risk, business-case-for-changing-rewards, make-financial-case-for-compensation, ….
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Answer slug from the query map (e.g. how-to-set-pay-ranges). |
| Name | Required | Description |
|---|---|---|
| slug | Yes | |
| sources | Yes | |
| contract | Yes | |
| question | Yes | |
| citations | Yes | |
| shortAnswer | Yes | |
| canonicalUrl | Yes | |
| problemFrame | Yes | |
| categoryTerms | Yes | |
| contentTargets | Yes | |
| relatedQuestions | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only and non-destructive behavior. The description adds useful behavioral context: this returns a fixed set of 13 authored pages, not arbitrary content, and describes the structured composition of each answer. This goes beyond the annotations without contradicting them.
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, front-loaded with the core behavior and followed by concrete enumerations. The slug list is useful and compact.
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 a single simple parameter, annotations, and an output schema, the description covers the essential calling context. The only minor gap is that not all 13 known slugs are enumerated, but the query-map reference and examples are sufficient for safe usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds beyond the schema by enumerating known slug examples, which helps the agent recognize valid inputs even though no enum is provided. This is meaningful extra value for the slug parameter.
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 precise verb ('Returns') and names the exact resource ('one authored /answers page') plus the content structure (question, short answer, problem frame, citations). This clearly distinguishes it from sibling tools like search_library or get_wage_jurisdiction.
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 through the list of known slugs and the single required 'slug' parameter, so the intended context is inferable. However, it does not explicitly name alternatives or state when not to use this tool, leaving selection guidance largely implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns one compensation library work's bibliographic record and (when profiled) its thesis.
| Name | Required | Description | Default |
|---|---|---|---|
| libraryId | Yes | Library catalog id (e.g. lib933f3afbe567c9ab). |
| Name | Required | Description |
|---|---|---|
| tags | Yes | |
| year | Yes | |
| pitch | Yes | |
| title | Yes | |
| author | Yes | |
| logline | Yes | |
| contract | Yes | |
| libraryId | Yes | |
| canonicalUrl | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish the tool as read-only and non-destructive. The description adds meaningful behavioral context: it returns a single record, and the thesis is included only 'when profiled,' which helps set expectations for conditional output. 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?
One sentence that conveys the core behavior and the conditional thesis inclusion without unnecessary detail. It is front-loaded, scannable, and appropriately sized for this simple tool.
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 parameter, full schema coverage, safe-read annotations, and an output schema present, the description is nearly complete. The only slight gap is that 'when profiled' is not elaborated, but the output schema likely clarifies the shape, so this is acceptable.
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 covers the only parameter fully with a clear description and example. The tool description does not add further parameter meaning, but the baseline of 3 applies because schema coverage is 100%.
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 ('Returns') and resource ('one compensation library work's bibliographic record'), and notes the conditional thesis inclusion. This distinguishes it from search_library, which is for finding works rather than retrieving a single record by ID.
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: call this when you need a single library work's bibliographic record by its library ID. However, it does not explicitly mention when to prefer this over siblings such as search_library 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.
Returns one tracked jurisdiction's current minimum-wage rate, effective date, validation status, and next scheduled step. 113 known jurisdictions, e.g.: federal, alaska, alabama, arkansas.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Jurisdiction slug (e.g. federal, california). |
| Name | Required | Description |
|---|---|---|
| name | Yes | |
| rate | Yes | |
| slug | Yes | |
| type | Yes | |
| source | Yes | |
| contract | Yes | |
| currency | Yes | |
| fullLabel | Yes | |
| stateCode | Yes | |
| typeLabel | Yes | |
| confidence | Yes | |
| displayName | Yes | |
| retrievedAt | Yes | |
| canonicalUrl | Yes | |
| scheduledRule | Yes | |
| classification | Yes | |
| effectiveStart | Yes | |
| validationStatus | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the read-only behavior is established. The description adds useful context by stating the tool returns exactly one jurisdiction's record and that there are 113 known jurisdictions, but it does not disclose additional behavioral caveats such as error behavior or lookups for unknown slugs.
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 two sentences, front-loads the essential return contents, and then provides useful scoping examples. There is no filler or repetition of the tool name or title.
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 tool with output schema available and safe annotations. The description covers what the tool returns, its single-input semantics, and the known slug space, so nothing critical is missing for an agent to select and 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 documents the 'slug' parameter with 100% coverage, so the baseline is 3. The description adds meaningful value by providing multiple concrete slug examples (federal, alaska, alabama, arkansas) and confirming a bounded set of 113 jurisdictions, which helps the agent infer valid slug format.
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 ('Returns') and a specific resource: one tracked jurisdiction's current minimum-wage record, including rate, effective date, validation status, and next scheduled step. It also gives the scope (113 known jurisdictions) and concrete examples, making it easy to distinguish from the unrelated sibling 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?
The description implies the tool's use case clearly: call it when you need the current minimum-wage details for a single jurisdiction identified by slug. It does not explicitly state when not to use it or name alternatives, but the sibling tools are unrelated, so no exclusion is necessary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List Compensation Professional's free machine-readable feeds and their MCP/REST availability. OpenAPI is also published at https://compensationprofessional.com/openapi.json.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful context about the feeds being free and machine-readable and includes the OpenAPI URL, but it does not discuss return format, pagination, or any other operational behavior. This is adequate but not rich.
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 two concise sentences with no wasted words. The primary purpose is front-loaded, and the OpenAPI URL is a relevant supplementary detail placed at the end.
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 zero-parameter, read-only listing tool, the description is nearly complete: it names the resource, the availability dimensions, and an external reference for more detail. It does not specify the exact shape of the returned feed list, but the tool is simple enough that this is a minor gap rather than a blocking one.
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 tool has zero parameters, so the schema imposes no interpretive burden. Per the rubric, a zero-parameter tool receives a baseline of 4. The description appropriately focuses on the tool's output rather than parameter syntax.
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 ('List') and a specific resource ('Compensation Professional's free machine-readable feeds and their MCP/REST availability'). The resource is distinct from the sibling tools that retrieve answers, library work, wage jurisdiction, or search the library, so an agent can tell this tool apart without opening schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states what the tool does but gives no guidance on when to use it versus the sibling tools. It does not mention that this might be the discovery step before calling get_* or search_* tools, and it lists no exclusions or alternative conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search or list the compensation-focused book library by title/author substring and/or classifier tag. No filters returns the first page.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | Case-insensitive classifier-tag substring. | |
| limit | No | Max rows to return (default 25, max 100). | |
| query | No | Case-insensitive title/author substring. |
| Name | Required | Description |
|---|---|---|
| tag | Yes | |
| count | Yes | |
| query | Yes | |
| total | Yes | |
| results | Yes | |
| contract | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds useful behavioral detail beyond the annotations, namely that omitting filters returns the first page, which helps set caller expectations for the default result set.
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 convey the core purpose, the filtering dimensions, and the no-filter behavior with no wasted words. The most important information is front-loaded in the first sentence.
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 read-only search tool with three optional, fully documented parameters and an output schema present, the description covers the core usage and the important no-filter case. The only minor gap is that pagination behavior beyond 'first page' is not explained, but the output schema and annotations carry much of the remaining context.
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 descriptions cover 100% of parameters, so the baseline is 3. The description mostly reinforces what the schema already says ('title/author substring', 'classifier tag') and adds minor semantic value by noting filters can be combined with 'and/or', but it does not substantially extend parameter understanding.
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 action ('Search or list'), identifies the resource ('compensation-focused book library'), and names the search dimensions (title/author substring and classifier tag). This clearly distinguishes it from sibling tools like get_library_work or get_answer.
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 the tool: when searching or listing library contents by title, author, or tag, and it clarifies that no filters returns the first page. However, it does not explicitly compare against sibling tools or state when not to use it, leaving some inference to the agent.
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.
get_answerget_library_workget_wage_jurisdictionlist_compensationprofessional_feedssearch_libraryClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
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