7 sales-outreach persona and skill products plus deliverability tooling over MCP: audit_copy scores email copy out of 100 for spam triggers and personalization, sequence_planner builds opt-out-respecting cadences, subject_line_scorer ranks up to 20 lines, utm_builder produces tracking URLs, and get_free_skill loads any product's complete free gateway skill.
Audit outreach copy for spam triggers and deliverability risks. FREE.
Checks spam-trigger phrases, length, structure, caps, links, and personalization signals. Typical input {"subject": "Quick question", "body": ""} returns {"score": 0-100, "findings": ["Spam-trigger phrases: ...", "Long for cold outreach (200 words; aim 50-125)"], "note": "..."}.
Use on the body of an outreach message. Not for the subject line alone (subject_line_scorer) and not for the send schedule (sequence_planner). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | The email body text to audit. | |
| subject | No | The email subject line; may be empty when auditing body copy alone. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds critical context beyond that: 'this tool never raises a protocol error — it returns an error object', the exact error format, and the safety of retry after correction. It also discloses that it's free ('FREE').
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 tightly packed: first sentence declares purpose, second lists checks, third shows example input/output, fourth clarifies proper use vs alternatives and error behavior. Every sentence earns its place. Front-loaded with key info.
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 all needed context: what it does, what it checks, example I/O, when to use, when not to, alternative tools, error handling behavior, and safety to retry. With output schema present and annotations rich, the description perfectly fills any remaining gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the description need not elaborate on each parameter. The description does add a typical usage example showing format and structure, which helps clarify usage but is not essential given the schema already fully describes both parameters. 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 states a specific verb ('Audit') and resource ('outreach copy for spam triggers and deliverability risks'), and clearly distinguishes from siblings like 'subject_line_scorer' and 'sequence_planner' by saying 'Not for the subject line alone' and 'not for the send schedule'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to use ('Use on the body of an outreach message'), when not to use ('Not for the subject line alone... not for the send schedule'), and names specific alternative siblings ('subject_line_scorer', 'sequence_planner'). It also gives a typical input example.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load a product's free gateway skill with its complete instructions. FREE.
Typical input {"slug": "linkedin-outreach"} returns {"slug": ..., "skill": "", "instructions": ""}.
Returns exactly one skill - the product's free gateway skill - chosen automatically from the slug, with no plan required. Use when the caller wants usable instructions immediately. Not for the product's other skills: those are named and need get_full_skill with a skill_name, which requires a paid plan. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''. Use list_products."}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug exactly as returned by list_products, e.g. "linkedin-outreach". |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint true and idempotentHint true. The description adds valuable behavioral context: returns exactly one skill chosen automatically, never raises protocol errors but returns an error object with fix instructions, and confirms it is always safe to retry after correcting input.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: purpose, example JSON, exclusivity and usage context, error behavior, and safety guarantee. Every sentence adds value without redundancy. 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?
Given a single required parameter, rich annotations, output schema present, and clear sibling context, the description covers all necessary aspects: input format, usage boundaries, error handling, and behavioral guarantees. Nothing 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 coverage is 100% and the schema describes 'slug' as 'Product slug exactly as returned by list_products'. The description reinforces this with an example ('{
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 loads 'a product's free gateway skill with its complete instructions', using a specific verb-resource pair. It distinguishes from the sibling tool 'get_full_skill' by noting the difference in skill selection and plan requirement.
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?
Explicit guidance is provided: 'Use when the caller wants usable instructions immediately' and 'Not for the product's other skills: those are named and need get_full_skill with a skill_name, which requires a paid plan.' This makes the choice between siblings unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load one product in full: its persona plus every paid skill. PREMIUM (license).
Typical input {"slug": "linkedin-outreach"} returns {"slug": ..., "name": ..., "persona": "", "skills": [{"name": ..., "instructions": ...}, ...], "free_skill": {...}}.
Returns persona plus every skill for one product. Use when the caller wants the whole product. Not for a single skill (get_full_skill) and not for a free look, which list_products and get_free_skill provide with no plan. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug exactly as returned by list_products, e.g. "linkedin-outreach". |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context beyond annotations: the non-protocol error behavior (returns error object instead of raising errors) and the exact retry semantics. Slightly redundant on idempotence but adds enough unique behavior detail.
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 front-loaded with the core purpose and example, then adds usage guidance and error behavior. Every sentence is informative, though the second paragraph slightly repeats the first paragraph's intent. Efficient overall, 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?
Given a single parameter, full schema coverage, rich annotations, an output schema, and clear sibling differentiation, the description leaves no gaps. It covers purpose, alternatives, error behavior, and retry safety. The agent has everything needed for correct 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% and the description reinforces the slug parameter with a concrete example ('linkedin-outreach') and tells the agent to use values from list_products. The description adds operational context (source of truth for valid inputs) beyond the schema's type definition, earning above 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 'Load one product in full: its persona plus every paid skill' and shows a typical input/output. It clearly distinguishes from siblings by naming get_full_skill and list_products/get_free_skill as alternatives.
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 provides explicit 'when to use' ('Use when the caller wants the whole product') and 'when not to use' ('Not for a single skill... not for a free look') with named sibling alternatives. The error handling section also tells the agent it's safe to retry after correcting input.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load one paid skill's complete instructions from a product. PREMIUM (license).
Typical input {"slug": "linkedin-outreach", "skill_name": "Connection Openers"} returns {"slug": ..., "skill": ..., "instructions": ""}.
Returns one named skill, selected by skill_name. Use when the caller wants one specific paid skill. Not for the free gateway skill, which get_free_skill returns with no plan, and not for every skill at once (get_full_product). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug exactly as returned by list_products. | |
| skill_name | Yes | Exact skill name as listed in that product's "skills" array from list_products. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only and idempotent behavior, but the description adds important context: the tool never raises a protocol error and instead returns an error object with a fix message. This disclosure goes beyond the annotations and is valuable for safe invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured, front-loaded with the core purpose, includes a concrete example, lists alternatives, and details error behavior. Every sentence adds value with no redundancy, achieving high conciseness.
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 presence of an output schema, the description does not need to detail return format. It covers error handling, licensing context (PREMIUM), and retry safety. Minor gap: no explicit mention that the skill must be paid, but that is clear from the initial sentence.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description provides an example of input and output, but does not add substantial meaning beyond the schema's own documentation (which already ties slug to list_products and skill_name to the skills array). The example reinforces usage but does not elevate the score.
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 loads one paid skill's complete instructions from a product. It specifies the verb 'Load' and the resource type, and distinguishes itself from sibling tools like get_free_skill and get_full_product by explicitly naming them.
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 says 'Use when the caller wants one specific paid skill' and explicitly excludes use cases for the free gateway skill and retrieving all skills at once, naming alternatives. However, it does not mention any prerequisites like needing to call list_products first, though the schema descriptions imply that.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the 7 outbound products with their included skills. FREE.
Takes no arguments. Returns a list of product objects, each {"slug": "linkedin-outreach", "name": ..., "tagline": ..., "skills": ["Skill A", ...], "free_skill": "Gateway Skill Name"}. Use the returned slug values with get_free_skill, get_full_product, or get_full_skill.
Returns metadata only - no persona text and no skill instructions. Use when the caller wants to see what this server covers. Not for keyword search across the whole 138-product catalog, which the catalog server's search_catalog does, and not for instructions the caller can act on (get_free_skill). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, but the description adds valuable behavioral context: return format is metadata only (no persona/skill instructions), errors are returned as objects rather than protocol errors, and retries are safe. This exceeds what annotations alone provide.
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?
Well-structured, front-loaded with the core function, then usage guidance, error behavior, and retry safety. Every sentence adds distinct value with no redundancy. Length is appropriate for the information density.
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, rich annotations, and an output schema, the description covers all essential aspects: what returns, what it excludes, error handling, and retry semantics. No gaps remain for correct invocation and interpretation.
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?
Description explicitly states 'Takes no arguments' and 'FREE', which is fully aligned with the empty schema. Since there are zero parameters, baseline is 4, but it earns a 5 by explicitly clarifying the input contract and referencing the output structure that uses returned slug values, adding meaning beyond 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?
States a specific verb and resource ('List the 7 outbound products with their included skills'), mentions the exact count and scope, and distinguishes itself from siblings by explicitly naming get_free_skill, get_full_product, and get_full_skill. Clear differentiation from the catalog search tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use ('when the caller wants to see what this server covers'), what not to use it for ('Not for keyword search... which the catalog server's search_catalog does'), and points to alternatives for actionable instructions. Also notes zero-argument simplicity and error-retry safety.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Plan an outreach cadence with expanding gaps and per-touch purposes. FREE.
Spaces N touches across D days, alternating channels. Typical input {"touches": 5, "days": 14, "channels": ["email", "linkedin"]} returns {"days": 14, "plan": [{"touch": 1, "day": 0, "channel": "email", "purpose": "open with a specific, researched reason"}, ...], "rule": "..."}.
Use when cadence and per-touch purpose are the question. Not for the wording of any single message (audit_copy). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Total span of the sequence in days; values outside 1-365 are clamped. Default 14. | |
| touches | No | Number of touches in the sequence; values outside 2-10 are clamped. Default 5. | |
| channels | No | Channel names to alternate through in order, e.g. ["email", "linkedin", "phone"]. Default ["email", "linkedin"]. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 description builds on these. It adds critical context: the tool never raises a protocol error but returns structured error objects, and it is safe to retry after correcting input. This surpasses what annotations alone convey.
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 (8 lines) and front-loaded with the core purpose and a key signal ('FREE'). The example is compact yet informative. Every sentence adds value: usage guidance, error behavior, and idempotency guarantee. 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?
Given the tool's moderate complexity (3 optional parameters, no enums, no nested objects) and the presence of a complete output schema, the description covers everything needed: purpose, usage boundaries, error handling, and idempotency. There are no gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters with defaults and ranges. The description's example input shows a typical call but does not add meaning beyond what is in the schema. 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 opens with a specific verb+resource: 'Plan an outreach cadence with expanding gaps and per-touch purposes.' It immediately distinguishes the tool from its sibling 'audit_copy' by explicitly stating what it handles (cadence planning) versus what it does not (message wording). The example JSON clarifies the exact output structure.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool ('Use when cadence and per-touch purpose are the question') and when not to ('Not for the wording of any single message (audit_copy)'). It names the alternative sibling tool, giving clear guidance for the AI agent to choose correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Score and rank up to 20 email subject lines, best first. FREE.
Scores 0-100 on length, spam triggers, caps, personalization merge fields, and curiosity cues. Typical input {"subjects": ["Quick question about {{company}}", "ACT NOW!!!"]} returns {"ranked": [{"subject": ..., "score": 92, "notes": ["personalized (+)", "question format"]}, ...]}.
Use when several subject lines need ranking against deliverability and curiosity signals. Not for article headlines, which the creator server's headline_analyzer ranks, and not for message bodies (audit_copy). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| subjects | Yes | List of candidate subject lines as plain strings; only the first 20 are scored. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnly, idempotent), the description adds that errors return a structured error message instead of protocol errors, and details scoring factors (length, spam, caps, personalization, curiosity). This extra context is valuable for safe invocation.
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 compact (four sentences) and front-loaded with the main action and key perk (FREE). Every sentence adds essential information without redundancy. Perfectly balanced.
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 simplicity (one parameter, output schema exists), the description covers purpose, usage scope, scoring criteria, error behavior, idempotency, and alternative tools. No obvious gaps remain; the agent has all needed 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?
The input schema already fully describes the single parameter with 100% coverage. The description reinforces the limit (first 20) and provides a concrete example, which adds clarity but does not significantly extend beyond the schema. Baseline 3, slight improvement for utility example.
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 scores and ranks email subject lines, specifies a limit of 20, and orders results best first. It also distinguishes from siblings like audit_copy and headline_analyzer, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use when ranking subject lines for deliverability and curiosity, and explicitly excludes article headlines (headline_analyzer) and message bodies (audit_copy). Also provides error handling and retry guidance, covering when to retry safely.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Build a UTM-tagged URL for campaign tracking. FREE.
Typical input {"url": "https://example.com/pricing", "source": "newsletter", "medium": "email", "campaign": "spring-launch"} returns {"tagged_url": "https://example.com/pricing?utm_source=newsletter& utm_medium=email&utm_campaign=spring-launch"}.
Use to build one tagged tracking URL. Not for analyzing campaign results and not for anything that belongs in the message body (audit_copy). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "url must start with http(s)://"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Destination URL; must start with http:// or https://. | |
| term | No | Optional utm_term value for paid keywords. | |
| medium | Yes | utm_medium value — the channel, e.g. "email" or "cpc". | |
| source | Yes | utm_source value — where the traffic comes from, e.g. "newsletter". | |
| content | No | Optional utm_content value for A/B variants. | |
| campaign | Yes | utm_campaign value — the campaign name, e.g. "spring-launch". |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations, the description discloses the error behavior ('never raises a protocol error — it returns {"error": ...}'), confirms idempotency and safety to retry, and mentions 'FREE'. This adds meaningful context about operation guarantees.
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?
Every sentence earns its place: purpose, cost, example, usage boundaries, and error handling. The structure is well-organized and free of fluff; the example is compact and illustrative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool shape, the description covers purpose, example input/output, error behavior, and exclusions. It is complete for an agent to invoke correctly, especially with the output schema available externally.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value with a concrete typical input/output example showing how parameters map to the tagged_url, and an error example clarifying url validation. This enriches parameter semantics 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 states 'Build a UTM-tagged URL for campaign tracking' with a specific verb and resource, and distinguishes from siblings by explicitly excluding 'analyzing campaign results' and 'anything that belongs in the message body (audit_copy)'.
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 clear when-to-use guidance ('Use to build one tagged tracking URL') and explicit exclusions with alternative context, naming audit_copy as the sibling for message body content. This prevents misuse.
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.
audit_copyget_free_skillget_full_productget_full_skilllist_productssequence_plannersubject_line_scorerutm_builderClaiming 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.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
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
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
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Each tool has a well-defined, unique purpose. The product/skill tools (list_products, get_free_skill, get_full_skill, get_full_product) are differentiated by scope and access level, and the writing tools (audit_copy, subject_line_scorer, sequence_planner, utm_builder) cover distinct aspects of outreach creation with no overlap. Descriptions explicitly state what each tool is not for, further reducing ambiguity.
All tool names use snake_case with a consistent verb_noun pattern: 'verb_object' for actions like list_products, get_free_skill, and 'noun_verb' for compound tools like subject_line_scorer. There is no mixing of styles or inconsistent abbreviations.
With 8 tools, the set is well-scoped for an outbound engine. It covers product discovery, skill retrieval, copy auditing, subject line scoring, sequence planning, and UTM building—neither too few to be useful nor too many to be overwhelming.
The tool surface appears complete for its stated domain: product/skill lookup, content quality checks (audit_copy, subject_line_scorer), cadence planning (sequence_planner), and campaign tracking (utm_builder). There are no obvious gaps for the knowledge and planning phase of outbound outreach.