io.github.dan24ou-cpu/agent-signal
NPM · AGENT-SIGNAL · 2 COMPONENTS
Collective intelligence for AI shopping agents — product intel, deals, and more
Available components
Last scanned (2 hours ago)
Scores higher than 55% of npm servers
Category E-commerce
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security81
- No malware found by supply-chain analysis.Pass
- CVE check failed: a known high-severity CVE affects zod 3.25.76, a direct dependency. A fixed version is available. View diagnostics → Fail
- No install/post-install scripts declared.Pass
- 43 of 137 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency39
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Maintenance check failed: the publisher has marked this version as deprecated on the registry, telling users to stop installing it. See how to fix → Fail
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability75
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 1445 tokens (~111/item across 13 items; 13 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management100
- No destabilizing schema changes in the last 30 days.Pass
Tool Coverage96
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 89% of tool parameters carry a description.Partial
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 13 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 13 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 3 Nov 26 0
- Stability: 0.97 → pass security
- 2 Nov 26 0
- Stability: pass → 0.97 functional
- 24 Oct 26 −5
- CVE-2023-54404 affects this package: high ▼ security
- Known CVEs: pass → fail ▼ security
- 21 Oct 26 0
- Stability: 0.97 → pass security
- 20 Oct 26 0
- Stability: pass → 0.97 functional
- 19 Oct 26 0
- Security disclosure: unverified → fail ▼ functional
- 18 Oct 26 0
- Security disclosure: fail → unverified ▼ functional
- 17 Oct 26 +2
- Stability: 0.83 → pass security
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed npm/agent-signal@0.2.1
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | npm |
Background: How many MCP packages publish verified provenance →
Vulnerabilities 1 finding
| ID | CVE | Severity | Vector | Fix available |
|---|---|---|---|---|
| GHSA-mrhx-c7xc-m3w5 | CVE-2023-54404 | high | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H | yes |
Background: What a vulnerability scan can and cannot prove →
Dependencies 137 packages
| Packages resolved | 137 |
|---|---|
| Stale | 43 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
check_merchant_reliability Check Merchant Reliability ~81
Check a merchant's reliability based on data from other AI agents' shopping sessions. Returns selection rate, stock reliability, match scores, and purchase outcomes. Use this to decide whether to trust a merchant's listings before recommending their products.
| Name | Type | Req | Description |
|---|---|---|---|
| merchant_id | string | yes | Merchant identifier to look up, e.g. 'amazon', 'bestbuy' |
No output schema declared.
No examples provided.
detect_deal Detect Deal ~107
Compare a product's current price against historical price data from all agents. Returns a verdict (best_price_ever, great_deal, good_deal, fair_price, above_average), savings vs average, and which merchants typically have the best prices. Use this before recommending a purchase to flag deals or overpricing.
| Name | Type | Req | Description |
|---|---|---|---|
| current_price | number | yes | The price you're seeing right now |
| product_id | string | yes | Product identifier, e.g. 'sony-wh1000xm5' |
No output schema declared.
No examples provided.
get_category_recommendations Get Category Recommendations ~98
Get intelligence about a product category from other AI agents' shopping sessions. Returns which products agents are selecting most, what decision factors matter, common requirements, and average budgets. Use this when starting a shopping task to understand what's working well in a category.
| Name | Type | Req | Description |
|---|---|---|---|
| budget_max | number | – | Optional budget ceiling to filter recommendations |
| category | string | yes | Product category, e.g. 'footwear/running', 'electronics/headphones' |
No output schema declared.
No examples provided.
get_constraint_match Match Constraints to Products ~128
Decision shortcut: find what products worked for agents with your EXACT constraints. Matches on specific requirements (e.g. 'wide fit + cushioned + under $150') and returns what those agents selected, why, and where to buy. Also returns broader recommendations from sessions with overlapping constraints. Use this to skip the search when a proven answer already exists.
| Name | Type | Req | Description |
|---|---|---|---|
| budget_max | number | – | Maximum budget |
| category | string | yes | Product category, e.g. 'footwear/running' |
| constraints | array | yes | Required attributes, e.g. ['wide fit', 'cushioned'] |
No output schema declared.
No examples provided.
get_product_intelligence Get Product Intelligence ~87
Get crowdsourced intelligence about a product from other AI agents' shopping sessions. Returns selection rate, common rejection reasons, which competitors beat it and why, price ranges seen, and outcome data. Use this before recommending a product to understand how other agents have evaluated it.
| Name | Type | Req | Description |
|---|---|---|---|
| product_id | string | yes | Product identifier to look up, e.g. 'sony-wh1000xm5' |
No output schema declared.
No examples provided.
get_session_summary Get Session Summary ~50
Retrieve a full summary of a shopping session including all product evaluations, comparisons, and the final outcome. Useful for reviewing what happened during a session.
| Name | Type | Req | Description |
|---|---|---|---|
| session_id | string | yes | Session ID to retrieve |
No output schema declared.
No examples provided.
get_similar_session_outcomes Learn from Similar Sessions ~126
Cross-agent learning: see what other AI agents chose when shopping for similar items with similar constraints. Returns which products were selected most often, what deciding factors mattered, and how sessions ended (purchased vs abandoned). Use this at the START of a shopping task to leverage collective agent intelligence.
| Name | Type | Req | Description |
|---|---|---|---|
| budget_max | number | – | Maximum budget to filter similar sessions |
| category | string | yes | Product category, e.g. 'footwear/running' |
| constraints | array | yes | Shopping constraints, e.g. ['lightweight', 'cushioned', 'wide fit'] |
No output schema declared.
No examples provided.
get_warnings Get Warnings ~94
Check for recent problems with a product or merchant before recommending them. Surfaces stock issues, high rejection rates, and abandonment signals from other agents. Returns warnings with severity levels (critical, warning, info) or all_clear if safe. Use this as a safety check before finalizing a recommendation.
| Name | Type | Req | Description |
|---|---|---|---|
| merchant_id | string | – | Merchant to check for warnings |
| product_id | string | – | Product to check for warnings |
No output schema declared.
No examples provided.
import_completed_session Import Completed Shopping Session ~124
Bulk-import an entire shopping session after the fact. Use this if you already completed a shopping task WITHOUT agent-signal tools and want to contribute your decision data retroactively. Accepts the full session in one call: intent, product evaluations, comparisons, and outcome. This helps the collective intelligence even when agent-signal wasn't used during the original task.
| Name | Type | Req | Description |
|---|---|---|---|
| comparisons | array | – | Comparisons made between products |
| evaluations | array | – | Products that were evaluated |
| outcome | object | – | Final outcome of the session |
| session | object | yes | Shopping session intent |
No output schema declared.
No examples provided.
log_comparison Log Product Comparison ~121
Log a comparison between two or more products during a shopping session. Call this when the agent explicitly compares products to decide between them. Record which product won and what the deciding factor was.
| Name | Type | Req | Description |
|---|---|---|---|
| deciding_factor | string | yes | The primary factor that decided the winner |
| dimensions_compared | array | – | Dimensions compared, e.g. ['price', 'reviews', 'durability'] |
| products_compared | array | yes | Product IDs being compared |
| session_id | string | yes | – |
| winner_product_id | string | yes | The product that won the comparison |
No output schema declared.
No examples provided.
log_outcome Log Shopping Outcome ~85
Log the final outcome of a shopping session. Call this when the shopping task ends — whether the user purchased, received a recommendation, abandoned, or deferred the decision.
| Name | Type | Req | Description |
|---|---|---|---|
| outcome_type | string | yes | – |
| product_chosen_id | string | – | Product ID if purchased or recommended |
| reason | string | – | Why this outcome occurred |
| session_id | string | yes | – |
No output schema declared.
No examples provided.
log_product_evaluation Log Product Evaluation ~172
Log that a product was evaluated during a shopping session. Call this for each product the agent considers, whether it's selected, rejected, or shortlisted. Include the rejection reason if the product was rejected.
| Name | Type | Req | Description |
|---|---|---|---|
| disposition | string | yes | Whether the product was selected, rejected, or shortlisted |
| in_stock | boolean | – | – |
| match_reasons | array | – | Why this product was a match |
| match_score | number | – | How well the product matches intent (0-1) |
| merchant_id | string | – | Merchant/retailer identifier |
| price_at_time | number | – | Price at time of evaluation |
| product_id | string | yes | Product identifier |
| rejection_reason | string | – | Why the product was rejected |
| session_id | string | yes | Session ID from log_shopping_session |
No output schema declared.
No examples provided.
log_shopping_session Log Shopping Session ~172
Start a new shopping session by logging the user's shopping intent. Call this at the beginning of any shopping task to capture what the user wants. Returns a session_id to use with subsequent log calls.
| Name | Type | Req | Description |
|---|---|---|---|
| agent_platform | string | – | Agent platform identifier |
| budget_currency | string | – | Budget currency code |
| budget_max | number | – | Maximum budget amount |
| category | string | – | Product category, e.g. 'footwear/running' |
| constraints | array | – | Required attributes, e.g. ['wide fit', 'cushioned'] |
| exclusions | array | – | Excluded brands or features, e.g. ['Nike'] |
| gift | boolean | – | Whether this is a gift purchase |
| raw_query | string | yes | The user's original shopping request |
| urgency | string | – | – |
No output schema declared.
No examples provided.
How do I install the io.github.dan24ou-cpu/agent-signal MCP server?
io.github.dan24ou-cpu/agent-signal runs locally as an npm package, launched with npx -y agent-signal. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · agent-signal
claude mcp add dan24ou-cpu-agent-signal -- npx -y agent-signal
What is the io.github.dan24ou-cpu/agent-signal MCP server?
io.github.dan24ou-cpu/agent-signal is an MCP server listed in the public MCP registry as io.github.dan24ou-cpu/agent-signal. Collective intelligence for AI shopping agents, product intel, deals, and more. This page covers its npm package (agent-signal).
Is the io.github.dan24ou-cpu/agent-signal MCP server safe to use?
io.github.dan24ou-cpu/agent-signal scores 77 out of 100 on VerifyMCP. We recorded 1 known advisory against it as of 5 November 2026. It declares no install or post-install scripts. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.dan24ou-cpu/agent-signal MCP server expose?
io.github.dan24ou-cpu/agent-signal exposes 13 tools: log_shopping_session, log_product_evaluation, log_comparison, log_outcome, get_session_summary, and 8 more. Their descriptions and schemas cost roughly 1,445 tokens of context every time the server is loaded.
Is the io.github.dan24ou-cpu/agent-signal MCP server still maintained?
io.github.dan24ou-cpu/agent-signal is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.dan24ou-cpu/agent-signal MCP server under?
io.github.dan24ou-cpu/agent-signal declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.