Talent-Augmenting Layer
Server Details
Personalised AI augmentation system — makes you better at your work, not dependent on AI
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- angelo-leone/talent-augmenting-layer
- GitHub Stars
- 0
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.9/5 across 15 of 15 tools scored. Lowest: 3.3/5.
Most tools have distinct purposes, but some overlap exists. For example, talent_get_profile and talent_status both provide user overviews, though status includes progression and warnings. talent_assess_create_profile and talent_save_profile both handle profile saving, but create_profile is specifically for assessment-generated profiles while save_profile is for general updates. The descriptions clarify these distinctions, preventing major confusion.
Tool names follow a highly consistent snake_case pattern with a clear 'talent_' prefix and descriptive verb_noun combinations. Examples include talent_assess_start, talent_get_profile, and talent_log_interaction. This uniformity makes the tool set predictable and easy to navigate, with no deviations in naming style.
With 15 tools, the count is well-suited for the server's purpose of talent assessment and management. It covers the full lifecycle from assessment (start, score, create_profile) to ongoing management (get_profile, log_interaction, get_progression) and organizational oversight (org_summary). Each tool appears necessary, with no obvious bloat or missing core functions.
The tool set provides comprehensive coverage for talent assessment and augmentation. It includes assessment initiation and scoring, profile CRUD operations (create, get, save, delete, list), interaction logging and telemetry parsing, progression tracking, calibration management, task classification, and organizational summaries. No significant gaps are evident; agents can handle end-to-end workflows without dead ends.
Available Tools
15 toolstalent_assess_create_profileAInspect
Generate and save a complete Talent-Augmenting OS profile from assessment data. Call this after talent_assess_score to create the profile file. Takes the computed scores, demographic info, goals, task classifications, and preferences collected during the assessment conversation. Returns the generated profile and saves it to disk.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | User's name | |
| role | Yes | Job role/title | |
| answers | Yes | Dict of item_id to score (same as talent_assess_score) | |
| industry | Yes | Industry description | |
| red_lines | No | Things AI should NEVER do for this user | |
| tasks_coach | No | Tasks where AI should coach, not do | |
| career_goals | No | List of career goals for the next 1-2 years | |
| organization | Yes | Company/org name | |
| tasks_augment | No | Tasks where AI accelerates the user's expert work | |
| tasks_protect | No | Tasks where AI must add friction to prevent de-skilling | |
| domain_ratings | Yes | Dict of domain name to expertise rating (1-5) | |
| feedback_style | No | Preferred feedback style | |
| learning_style | No | Preferred learning style (socratic, direct, examples, balanced) | |
| tasks_automate | No | Tasks to fully automate with AI | |
| context_summary | No | 1-3 sentence summary of the user's work context | |
| tasks_hands_off | No | Tasks that should stay fully human | |
| skills_to_develop | No | Skills the user wants to grow | |
| skills_to_protect | No | Skills at risk of atrophy from AI over-reliance | |
| communication_style | No | Preferred communication style |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that the tool saves the profile to disk (persistence). With no annotations beyond a title, the description provides minimal behavioral context. No mention of required permissions, side effects, or error handling.
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, front-loaded with purpose, then usage, then outcome. No redundant words. Efficient and scannable.
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?
Covers return value and file persistence. For a tool with 19 parameters and no output schema, it could explain parameter relationships or constraints. However, schema descriptions are thorough, so overall adequate.
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 covers 100% of parameters with descriptions, so baseline is 3. Description only groups parameters vaguely ('demographic info, goals') without adding new detail beyond what 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?
Description clearly states the verb ('Generate and save'), resource ('profile'), and context ('from assessment data'). Distinguishes from siblings like talent_assess_score and talent_save_profile by specifying the step in the workflow.
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 to call after talent_assess_score, providing clear sequential context. Does not mention when not to use or compare to alternatives like talent_save_profile.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
talent_assess_scoreAInspect
Compute the user's TAOS assessment scores from the numeric answers collected during the assessment conversation. Takes the per-question answers (each 1 to 5) and per-domain expertise ratings, and returns the user's dependency risk, growth potential, AI literacy, expertise summary, and overall TAOS readiness score, with plain-language interpretations and recommended coaching calibration. Call this once all assessment questions have been answered, then pass the result to talent_assess_create_profile.
| Name | Required | Description | Default |
|---|---|---|---|
| answers | Yes | Dict of item_id to score (1-5). Keys: A1-A5 (dependency risk), B1-B5 (growth potential), D1-D4 (AI literacy). Example: {"A1": 3, "A2": 4, "B1": 5, "D1": 3, ...} | |
| domain_ratings | Yes | Dict of domain name to expertise rating (1-5). Example: {"Writing": 4, "Strategy": 3, "Stakeholder engagement": 2} |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses the output format and purpose, but does not explicitly state if the tool is read-only or has side effects. However, the context implies a computation step, and the detailed output description adds 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?
Three concise sentences covering what it does, inputs, outputs, and usage sequence without extraneous 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?
Describes all return components and pipeline placement, but lacks error handling or input validation details. Nevertheless, sufficient for an AI agent to 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%, but the description adds value by providing examples and explaining key structures (e.g., item_id format, domain name examples), aiding understanding 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 the tool computes TAOS assessment scores from numeric answers, specifies the resource (user's scores), and distinguishes it from sibling talent_assess_create_profile by indicating the calling order.
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 when to call: after all assessment questions answered, and instructs to pass result to talent_assess_create_profile, providing clear usage context and next step.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
talent_assess_startAInspect
Start a Talent-Augmenting OS onboarding assessment. Returns the full assessment protocol with all questions, behavioural anchors, and instructions for how to run the assessment conversationally. The chatbot uses this to ask questions one at a time, collect answers, then call talent_assess_score and talent_assess_create_profile to compute scores and save the profile. Call this at the beginning of any onboarding conversation.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Name of the person being assessed (optional: can be collected during the assessment) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that it returns the full assessment protocol with questions, anchors, and instructions. No annotations beyond title, so description fills the gap. No contradictions.
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 concise sentences: what it does, what it returns, and when/how to use. 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?
Even without an output schema, it fully describes the return value and the next steps. For a simple tool with one optional param, all necessary context is present.
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 only parameter 'name' has schema description, and the description adds value by noting it's optional and can be collected during assessment, which is not in 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?
Clearly states it starts an onboarding assessment and returns the protocol. Distinguishes from siblings by describing the workflow (talent_assess_score, talent_assess_create_profile).
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 'Call this at the beginning of any onboarding conversation.' Describes the sequential calling pattern. Lacks explicit when-not-to-use but provides clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
talent_classify_taskAInspect
Classify a task according to the user's Talent-Augmenting OS profile. Returns one of: automate, augment, coach, protect, hands_off: along with the recommended AI behaviour for that task.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | User's name | |
| task_description | Yes | Description of the task to classify |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only provide a title, so description carries full burden. It describes the return value but does not disclose whether the tool is read-only, requires authentication, or has side effects. 'Classify' suggests no mutation, but this is not stated explicitly.
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, front-loaded with the action and output. Every word is necessary: states purpose, output categories, and additional info (recommended AI behaviour). No fluff.
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 low complexity (2 string params, no output schema), the description adequately explains what it does and what it returns. It lists the five classification categories and mentions 'recommended AI behaviour', which is sufficient for a simple classification tool. Minor gap: could clarify the format of the AI behaviour.
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% with both parameters described in the schema. The description does not add new semantic information beyond what the schema already provides (e.g., 'User's name', 'Description of the task'). No examples or formats are given.
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 verb 'Classify a task' and resource 'user's Talent-Augmenting OS profile'. Lists the five possible classification categories, making the tool's purpose specific and distinct from sibling tools which deal with profile management, scoring, 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?
No explicit when-to-use or when-not-to-use guidance. The description implies usage for classifying any task, but does not mention prerequisites, alternatives, or context for choosing this tool over similar ones like talent_suggest_domains.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
talent_delete_profileBInspect
Delete a user's profile and interaction logs.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | User's name |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description indicates a destructive action but provides no details on reversibility, permissions, or side effects beyond profile and log deletion. With no annotations, the description should carry more behavioral context.
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 wasted words. It is concise and front-loaded with the core action.
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 delete tool with one parameter and no output schema, the description covers the basic purpose but lacks information on response format or potential impacts. It is adequate but not comprehensive.
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 has 100% coverage with a description for the 'name' parameter. The tool description adds no additional meaning beyond the schema, so baseline 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 states the tool deletes a user's profile and interaction logs, using a specific verb and resource. It distinguishes from siblings as none of the other tools have a delete function, but it does not explicitly differentiate.
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 is provided on when to use this tool versus alternatives like talent_save_profile or talent_get_profile. No prerequisites or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
talent_get_calibrationCInspect
Get the Talent-Augmenting OS calibration settings for a user. Returns a compact JSON block suitable for injecting into any LLM system prompt. Includes friction levels, coaching domains, red lines, and interaction preferences.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | User's name |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations lack readOnlyHint or destructiveHint, so the description must convey safety and side effects. It does not state that the operation is read-only, requires any permissions, or lack side effects. It only describes the output content.
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 (two sentences), front-loaded with the action and resource, and includes details on output content. It is efficient but could be slightly more structured.
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 1-parameter tool with no output schema, the description provides partial compensation by listing output fields. However, it lacks details on error behavior, return structure, or edge cases (e.g., missing user).
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% with a single parameter 'name' described as 'User's name'. The description adds no additional semantics, format, or constraints beyond the schema, earning a baseline score of 3.
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 retrieves 'Talent-Augmenting OS calibration settings' and specifies the output is a compact JSON block for LLM system prompts. The verb 'Get' and resource are explicit, but it does not distinguish from sibling getters like talent_get_profile.
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 such as talent_get_profile or talent_get_progression. The description implies it is for obtaining calibration settings but offers no context on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
talent_get_profileAInspect
Load a Talent-Augmenting OS profile by name. Returns the full profile with expertise map, calibration settings, task classification, and red lines. Use this at the start of every conversation.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | User's name (e.g., 'Angelo') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description implies a read-only operation ('load'), but annotations lack behavioral hints like readOnlyHint or destructiveHint. There is no mention of side effects, permissions, or rate limits, so the description only partially fulfills the transparency burden.
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: the first clearly states purpose and return values, the second provides usage context. No extraneous information; every sentence 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 simple read operation with one parameter and no output schema, the description adequately lists the returned components. It is complete for an AI agent to understand when and how to invoke the tool.
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% for the single parameter 'name', with a description in the schema. The main description adds no additional semantic detail beyond what is already in the schema, so it meets the 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 verb 'Load' and resource 'Talent-Augmenting OS profile by name', and lists the returned components (expertise map, calibration settings, etc.). It is distinct from sibling tools like talent_save_profile or talent_delete_profile, though it does not explicitly differentiate.
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 instructs 'Use this at the start of every conversation,' providing clear when-to-use guidance. However, it does not mention when not to use it or contrast with alternatives like talent_list_profiles to find a profile name first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
talent_get_progressionAInspect
Get skill progression analysis for a user. Shows interaction counts, engagement patterns, domain-level growth/atrophy signals, and warnings about potential de-skilling.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | User's name |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal (only title), so the description carries the burden. It lists outputs like interaction counts and warnings, but doesn't disclose read-only nature, auth requirements, or rate limits. 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?
A single sentence that front-loads the purpose and lists key outputs. No wasted words; efficient and clear.
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?
No output schema exists, so the description compensates by listing outputs: interaction counts, engagement patterns, growth/atrophy signals, warnings. It could mention time range or data recency, but is reasonably complete for a one-parameter tool.
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% for the single parameter 'name', described as 'User's name'. The description adds no extra meaning beyond what the schema provides, so baseline 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 states the tool gets skill progression analysis for a user, using a specific verb and resource. It distinguishes from siblings like talent_get_profile and talent_get_calibration by focusing on progression metrics.
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 for progression analysis but provides no explicit guidance on when to use it versus alternatives. No when-not-to-use or exclusion criteria mentioned, leaving the agent to infer based on task context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
talent_list_profilesAInspect
List all available Talent-Augmenting OS profiles.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no readOnlyHint or destructiveHint. The description does not disclose behavioral traits such as read-only nature, pagination, or side effects. As a list tool, it likely is read-only, but the description fails to affirm this.
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, efficient sentence that is front-loaded. However, it omits potentially useful information about the tool's behavior.
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 zero parameters and no output schema, the description adequately states the tool's function. However, it lacks behavioral context, such as whether the list is paginated or requires specific permissions.
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?
No parameters are defined, so baseline applies. The description adds no parametric detail, but none is needed.
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 'all available Talent-Augmenting OS profiles', clearly distinguishing from sibling tools like talent_get_profile (specific profile) and talent_delete_profile (delete).
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 use for obtaining a full list of profiles, and sibling names provide context. However, no explicit when-not-to-use or alternatives are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
talent_log_interactionAInspect
Log an interaction for skill tracking. Call this after substantive AI interactions to track the user's engagement patterns and skill development.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | User's name | |
| notes | No | Optional notes about the interaction | |
| domain | Yes | Which skill domain? | |
| skill_signal | Yes | What skill signal was observed? | |
| task_category | Yes | Which task category was this interaction? | |
| engagement_level | Yes | How critically did the user engage? |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Only annotation is a title; no readOnlyHint or destructiveHint. Description does not disclose side effects, data persistence, authentication needs, or whether the interaction is stored permanently. Minimal behavioral 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?
Two sentences, front-loaded with the core action ('Log an interaction'), no unnecessary words. Efficient and focused.
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?
No output schema; description does not explain return values, error cases, or confirmation of logging. For a tool with 6 parameters and 5 required fields, the description lacks completeness about what happens after invocation.
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% with clear descriptions for each enum and field. The description adds no additional parameter meaning beyond the schema, meeting the baseline of 3.
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 explicitly states the verb 'Log' and the resource 'interaction for skill tracking', and mentions 'after substantive AI interactions' which distinguishes it from sibling tools like talent_assess_score or talent_classify_task.
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?
Provides clear guidance to call 'after substantive AI interactions', implying post-interaction context. No explicit exclusions, but sibling tools are distinct (e.g., assessment, profile management), so usage context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
talent_org_summaryAInspect
Get an organisation-level summary across all profiles. Shows aggregate dependency risk, growth potential, expertise distribution, trend alerts, and per-domain skill breakdown. For org dashboards.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations beyond title are provided, so the description must carry the burden. It explains the tool is read-only (shows aggregate data) without side effects, but lacks details on data freshness, permissions, or whether it resets any state. Adequate but not comprehensive.
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 filler. First sentence states the action and scope, second lists key outputs. Efficient and front-loaded.
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 tool with no output schema, the description lists the main components of the summary. It is fairly complete, though it could mention if the result is a single object or an array, and any pagination. Minor gap.
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?
There are no parameters, so schema coverage is 100%, giving a baseline of 4. The description adds value by specifying the content of the summary beyond the empty 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 it retrieves an organisation-level summary across all profiles and lists the included metrics (dependency risk, growth potential, etc.). It distinguishes itself from sibling tools that focus on individual profiles or assessments.
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 phrase 'For org dashboards' provides context on when to use this tool, implying it is for high-level overviews. However, it does not explicitly exclude scenarios like individual profile queries or mention alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
talent_parse_telemetryAInspect
Parse telemetry blocks from an LLM response and record them. The system prompt instructs the LLM to emit JSON blocks after each substantive interaction. Call this tool with the full LLM response text to extract and log all telemetry entries. Each entry is saved to the local JSONL interaction log and optionally pushed to the hosted API.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | User's name (for profile-linked logging) | |
| session_id | No | Optional session identifier for grouping turns | |
| response_text | Yes | The full LLM response text containing <tal_log> blocks |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no safety hints, so the description carries full burden. It discloses that data is saved locally and optionally pushed to an API, indicating write operations. However, it lacks details on idempotency, error handling, or required permissions.
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 three sentences that are front-loaded: first sentence states the purpose, second provides context, third gives usage instruction. Every sentence earns its place 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?
The description covers the basic usage and outcome (saving to log), but it does not mention what the tool returns (no output schema) or error conditions like missing <tal_log> blocks. For a modest tool, this is adequate but not fully complete.
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 description does not add significant meaning beyond what is already in the schema. It confirms that response_text should contain <tal_log> blocks, but this is already implied by the schema description.
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 that the tool parses <tal_log> telemetry blocks from an LLM response and records them. The verb 'parse' and resource 'telemetry blocks' are specific, and the tool is distinct from siblings like 'talent_log_interaction' which likely handles general interaction logging.
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 gives implicit guidance by stating to call with the full LLM response text to extract telemetry entries, but it does not explicitly state when not to use this tool or mention alternatives (e.g., talent_log_interaction) for other logging needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
talent_save_profileAInspect
Save or update a user's profile markdown content. Use this after running /talent-assess to write the generated profile, or after /talent-update to save changes.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | User's name | |
| content | Yes | Full profile markdown content |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal (only title), so description should disclose behavioral traits. It does not mention idempotency, overwrite behavior, or authorization needs. Basic 'save' description lacks depth.
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 that front-load the main purpose and usage context. No superfluous words, every sentence 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 simple save tool with no output schema, description covers purpose and usage context. Lacks details on conflict resolution or return value, but adequate given simplicity.
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%; parameters 'name' and 'content' are described in schema. Description adds no additional meaning beyond 'profile markdown content' already present 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?
Description clearly states 'Save or update a user's profile markdown content', specifying the exact action and resource. It distinguishes from siblings like talent_delete_profile and talent_assess_create_profile by its save/update function.
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 when to use: 'after running /talent-assess to write the generated profile, or after /talent-update to save changes.' This provides clear context and implies alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
talent_statusAInspect
Get a comprehensive status report for a user: profile summary, current calibration, skill progression stats, trend direction, atrophy warnings, and recommended next actions. Use this for a quick overview at the start of a conversation.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | User's name |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only provide a title, so the description bears full responsibility. It lists what data is included but does not disclose read-only nature, permissions, or limitations. The title 'Get User Status' hints at read operation, but explicit behavioral details are missing.
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 with the main verb and resource front-loaded. Every word serves a purpose: describes content and provides usage guidance. No 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 one-parameter tool with no output schema, the description adequately explains what the report contains and when to use it. Could mention that it is a read-only operation, but overall complete enough for selection.
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 covers the single parameter with description 'User's name'. The description adds only 'for a user', which is essentially the same. Baseline 3 since schema coverage is 100% and no additional semantic value.
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 returns a comprehensive status report listing specific components (profile, calibration, skills, trends, warnings, actions). It distinguishes from sibling tools that focus on individual aspects.
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 recommends use 'at the start of a conversation' for a quick overview, implying when to use. Does not mention when not to use or list alternatives, but context from siblings suggests specific tools for deeper dives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
talent_suggest_domainsAInspect
Suggest expertise domains for a user based on their role, industry, and responsibilities. Returns a curated list of domain suggestions with descriptions drawn from an industry-specific taxonomy. Use this during the assessment to help identify relevant domains for the Expertise Self-Assessment (ESA). The LLM has override authority and can add or remove domains from the suggestions.
| Name | Required | Description | Default |
|---|---|---|---|
| role | Yes | Job title or role description | |
| industry | Yes | Industry or sector | |
| responsibilities | No | Optional description of key responsibilities |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes return type and mentions LLM override authority, which is a key behavioral trait. Annotations provide only a title, so description carries the burden; no contradictions.
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, no wasted words, front-loaded with purpose. Every sentence adds value: what it does, when to use it, and a critical behavioral note about override authority.
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?
Description fully covers inputs, outputs, usage context, and a key behavioral aspect. No output schema exists, so the description appropriately explains return format. Complete for a 3-parameter tool with clear purpose.
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 descriptions are already present. Description repeats the parameter roles without adding new semantic detail beyond the schema, meeting baseline expectations.
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 suggests expertise domains based on role, industry, and responsibilities, and returns a curated list with descriptions from a taxonomy. This distinguishes it from sibling tools like talent_assess_score or talent_assess_start.
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?
Specifies use during assessment for the Expertise Self-Assessment (ESA), providing clear context. However, it does not explicitly mention when not to use or alternative tools, though none are obvious given sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
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Feature your server to boost visibility and reach more users
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
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If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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